rafmacalaba/data-use-mentions-extended huggingface dataset:
*

|Divergence of Exports - Regional exports<br>that match exports to the ROW as a<br>share ofthelatter *|**India**<br>18%|**Pakistan**<br>22%|**Bangladesh**<br>1%|**Sri Lanka**<br>10%|**Nepal**<br>0%|
|---|---|---|---|---|---|

19 The products ranked on growth levels are chosen as they better reflect the dynamic products than mere trade values. The cut-off point of 75 percent was chosen to exclude marginal products that may not be reported regularly. The SITC 4- digit product classification is the highest level of disaggregation for which the comparisons can be carried out because consistent reporting of data is inaccessible at further disaggregated levels, such as the SITC or Harmonized 6 to 8-digit level.

25
For each question, public officials were permitted to state, “Don’t Know”, though the enumerator did not suggest this as a response. In 23% of questions, the respondent stated that they simply did not know and could not provide a valid guess. This is in contrast to the fact that the quantities assessed are the core variables required to make policy, planning and distribution decisions in accordance with the national sector plans.

# **2.4 Objective Assessments of Citizen Characteristics**

For each indicator, we have administrative or survey data that can benchmark the responses by officials. The specific sources for each variable are outlined in Table A2 and are the latest Census, the latest available service delivery data from the education and health management information systems, and the Woreda and City Benchmarking Survey (WCBS). Each data set is created by sourcing information directly from service providers and aggregating it at the center of government. The sources we use are generally regarded as the highest quality large-scale measurement undertaken in Ethiopia on the topics outlined above. For example, in education, the Education Management Information System is comprised of school-level data that are processed through each tier of government, officials of which undertake quality checks to ensure that the data are as accurate as possible. The WCBS data was collected in collaboration with the World Bank and the Ministry of Public Service and Human Resource Development as a monitoring tool over five separate waves of data collection. All of this data is available to the officials we study through the relevant directorates of their tier of government at the minimum, and in some cases online.<sup>10</sup> We take the administrative and survey data as a proxy for the underlying state of the world. We use these variables to benchmark the claims of public officials. For each indicator, we subtract the corresponding value in the benchmark data from the relevant claims in our data set, such that a positive error is an overestimate.

Our core analysis aims to bring together the full
.8%|
|Private/public|117.5%|87.7%|188.1%|172.4%|n.m.|
|Housing costs-to-income***||||||
|- owners|11.0%|n/a|13.2%|17.4%|9.1%|
|- public rental|34.3%|n/a|16.8%|17.6%|10.1%|
|- private rental|35.5%|n/a|21.6%|19.4%|27.6%|
|Private/public|103.4%|n/a|128.9%|110.1%|273.4%|

_Source_ : Household surveys (Table 1). _Note_ : Urban apartments only, housing bill excluding energy/heating. **Average rent payment divided by average income proxy value. ***Average housing costs (rent/condominium fees + utilities) divided by average income proxy value.

However, the results from the surveys (Table 9) provide a useful snapshot of average existing contract rents in the popular market. Since rent levels are derived from interviews with sitting tenants holding seasoned contracts, they reflect different contract originations (vintages) and will differ from current new contract rent levels. The difference between new and existing contract rent levels will be influenced by locally specific tenure durations and the presence or absence of rent controls. However, in cross-country comparisons, average rents do not seem to go too far astray from the anecdotal data on new rents.

The exception is Poland, which is the only studied country exhibiting both lower private than public rents and significantly lower average rents than marginal (new contract) rents (Table 9). Private rents reflect a market-mimicking grading scale used by many municipalities for calculating rent ceilings below the overall ceiling of 3 percent of replacement costs,
.6|1.4***|1.0***|1.8**|
|(Percentage points)|[-0.4, 1.5]|[-0.3, 0.9]|[-0.6, 2.6]|[0.6, 3.4]|[0.5, 1.8]|[1.0, 4.3]|

Note: Table shows medians, as well as interquartile ranges (i.e., two numbers in brackets), of macroeconomic and debt sustainability indicators across completed government debt booms and ongoing booms in respective country groups. Indicators on macroeconomic outcomes are annual averages during debt booms. In indicators on debt sustainability, initial and changes refer to, respectively, the level at the beginning, or trough, of debt booms and average annual changes during debt booms. *, **, and *** denote that medians are statistically significantly different between completed and ongoing booms in respective country groups at the 10 percent, 5 percent, and 1 percent levels, respectively.

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|Poorest 18 percent<br>nationally|70 percent|6.5 percent|
|Low confidence, small<br>program|Poorest 18 percent<br>nationally|50 percent|13.2 percent|
|High confidence, medium<br>program|Poorest 25 percent<br>nationally|90 percent|0.7 percent|
|Medium confidence,<br>mediumprogram|Poorest 25 percent<br>nationally|70 percent|6.5 percent|
|Low confidence, medium<br>program|Poorest 25 percent<br>nationally|50 percent|15.4 percent|
|High confidence, large<br>program|Poorest 35 percent<br>nationally|90 percent|3.3 percent|
|Medium confidence,<br>largeprogram|Poorest 35 percent<br>nationally|70 percent|13.2 percent|
|Low confidence, large<br>program|Poorest 35 percent<br>nationally|50 percent|19.9 percent|

_Data Source_ : Multi Cluster Needs Assessment (MCNA) survey The lower level of overlap can potentially be explained by the fact that the likely eligibility exercise is carried out on a survey rather than the beneficiary’s database. First, even though there are a greater number of common variables in the two models, different enumeration may have led to questions being asked in different ways. For example, household size depends critically on what a respondent considers a household to be. For some, this may mean a family, for others multiple families living under the same

> 14 The updated SEVAT models provide three cut-off thresholds designed such that those deemed highly vulnerable would receive transfers up to three times per year, moderate vulnerable receive two times, while those deem just vulnerable receive a single transfer in a year. The overlap presented in Table 9 are for highly vulnerable households only (14 percent of the sample). Results are similar, decreases slightly, when including other two groups.

19
Figure 1. Average annual resource revenues share in GDP <!-- Start of picture text --> 15.5<br>16<br>14<br>12<br>10<br>8.6<br>8<br>6 4.8<br>4<br>2.1<br>2<br>0<br>Rest of the World Sub-Saharan Africa<br>non-RR RR<br>% GDP<br><!-- End of picture text --> Notes: Natural resource revenues share of GDP and in constant 2015 US$, 2004-2014 annual average. SubSaharan Africa RR includes 21 countries listed in IMF (2012) as resource rich and Sub-Saharan Africa non-RR includes 9 countries non listed in IMF (2012) with non-missing data. Source: authors’ calculations with data from UNU-WIDER Government Revenue Dataset 2021.

Figure 2: Average annual natural resource revenues, by country, between 2004-2014 <!-- Start of picture text --> 30 50<br>45<br>25<br>40<br>35<br>20<br>30<br>15 25<br>20<br>10<br>15<br>10<br>5<br>5<br>0 0<br>% of GDP (blue bars)<br>Angola Gabon Nigeria Chad Sudan Guinea Mali Niger Namibia Zambia Liberia Ghana Uganda Constant 2015 US$, billions (red bars)<br>Congo, Rep Equatorial Guinea Botswana South Sudan Sao Tome and Principe Mauritania Cameroon Congo, Dem Rep Cote d'Ivoire Zimbabwe Sierra Leone<br><!-- End of picture text --> _Notes: Natural resource revenues share of GDP (blue bars) and in constant 2015 US$ (red bars) for 2004-2014 annual average by resource-rich Sub-Saharan African country with natural resource revenues greater than zero. The following six countries from the resource-rich group
**Figure 7. Performance and Poverty and Inequality Impacts of the MSA and Heating Allowance under Baseline Scenario (pre-reform)**

<!-- Start of picture text --> a. Generosity (benefits as % of MYPP) b.Inequality, poverty, and poverty gap impact<br>Marginal Contributions to<br>14.6 Inequality 0.00<br>Change in Poverty Gap 0.51<br>Marginal Contributions to<br>0.18<br>2.0 Poverty<br>0.5 0.0 0.0<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by taking the difference between the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._ Poverty and Distributional Impacts of the Policy Reform _How restrictive are the income thresholds after the Reform? A comparison with the Bulgarian Food Basket_

**This section examines the stringency of the income thresholds and parameters established for the Monthly Social Allowance (MSA) program.** To gauge this, we compare the income thresholds with the cost of a standard Bulgarian food basket. This comparative assessment provides valuable insights into the MSA program's adequacy in meeting eligible individuals and households' basic nutritional and sustenance needs.

**In principle, the core MSA benefit should guarantee individuals access to a comprehensive set of goods and services that satisfy the minimum living standards accepted by society.** This encompassing benefit covers a full consumption basket necessary to meet basic needs. Therefore, to determine the adequacy of a means-tested benefit, the income level of a Bulgarian family can be compared to the amount
will apply profit maximizing levels of inputs so that productivity will be independent of their endowments. If households are credit constrained, inputs will be used at less than the productivitymaximizing level and higher levels of liquidity would be associated with higher productivity of resource use. This implies that, at the margin, and to the extent that they do not affect credit constrained status, increases in certain factors may reduce rather than increase productivity in terms of output per hectare. For example, a marginal increase in operated land size could, for constrained households, reduce productivity due to its negative impact on the intensity of variable input use in the face of binding credit constraints. Similarly, the impact of changes in family labor endowments could have differential impacts depending on whether households are credit constrained or not and whether non-agricultural labor markets are accessible and function well.

# **3. Data and descriptive statistics**

Explicit elicitation of different types of constraints in the fourth round of a longitudinal rural household survey in Ethiopia’s Amhara region suggests that a large number of producers are credit constrained and that, in addition to supply-side considerations, high levels of risk are a key factor. While constrained and unconstrained farmers differ in predictable ways (e.g., unconstrained ones are more affluent), agricultural productivity is higher for unconstrained farmers only in one of the two regions. Being credit constrained is thus not always synonymous with failing to achieve full productive potential.

# **3.1 Sample and nature of data used**

Our analysis is based on the fourth round of a longitudinal rural household survey in the Amhara region of Ethiopia’s highlands conducted by the Ethiopian Development Research Institute (EDRI), Gothenburg University, and the World Bank in 2007. The survey includes information on demographics, resource endowments, agricultural production, and input, output, and credit markets participation for 1700 randomly selected households in 14 villages ( _kebeles_ ) in six districts ( _woredas_ ) in South Wollo and East Gojam zones of Amhara (Deininger _et al.
not statistically significant).

In a nutshell, these findings are in line with the notion that the surge in violence in areas treated by the program is substantially driven by the activities of international terrorist groups such as Boko Haram. To the extent that multiple media reports have shown that jihadist groups have deliberately targeted aid workers and government services such as schools or

> 19The appendix contains a series of robustness checks. It shows that the results are robust with respect to alternative definitions of conflict in the GDELT data set (Table A2), alternative coding of small villages (Tables A3–A4), and alternative geographical matching (Table A5). In Tables A6–A7, we also reproduce our results using an alternative data source, the Armed Conflict Location Event Data Project (ACLED) data set (Raleigh et al. 2010). We include severe conflict events involving battles, explosions/remote violence, protests, riots and violence against civilians (ACLED 2019). ACLED is less fine grained and contains fewer events than GDELT. For instance, 6 percent of communities are within 10km from a conflict event reported by GDELT, but 2.7 percent of communities are within 10km from a conflict event reported by ACLED. While in Table A6—which draws exclusively on ACLED—the coefficients of interest maintain the expected sign but lose statistical significance, in the following Table A7—which pools ACLED with GDELT data—results remain statistically significant.

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# **3.1 Tax Administrative Data**

Administrative data from the TRA was utilized to analyze the impact of the intervention on taxpayers’ behavior. This data comprised of payment records for presumptive taxpayers (those with a turnover below 100 million) from January 2022 to March 2024. The data was reshaped from long to wide format, aggregating payments by the same individual within the same period to ensure a single sum per unique ID.<sup>1</sup> The reshaped data comprised 408,106 businesses.

Since randomization was implemented at the ward level, a taxpayer mapping exercise was necessary to estimate treatment effects, as the administrative data did not include ward information. However, the data contained other geographic identifiers such as street locations, regions, and tax districts. These identifiers allowed for matching each unique street location in the administrative data to its corresponding ward. The mapping process began by obtaining data from the National Bureau of Statistics (NBS), one for each region of mainland Tanzania. This data contained all location names within each region, from the highest to the lowest subnational division (containing columns for Region, Ward, Street, Village, and Neighborhood). Next, the street locations in the TRA data were cleaned by removing non-alphanumeric characters, punctuation, and special symbols, inserting spaces to separate strings, and converting all text to a common case. The matching process aimed to find the closest match between street locations from the administrative data and the Ward, Street, Village, and Neighborhood information from the NBS data, as unique street locations in the administrative data could signify not only a street but also a ward, village, or neighborhood. Using the Jaro-Winkler distance measure, matches with a similarity score below 0.3 were retained, and matching was restricted to the same region. This process identified the ward name corresponding to each street location in the TRA data, producing an output called the bridge, which linked street locations from the administration data with their corresponding wards as specified
society) to monitor the environmental impact of their offshore production or subcontractors in China;

- level and predictability of income – see for example the data collected by the World Economic Forum (WEF) in the context of the New Vision for Agriculture that suggest a significant increase in productivity and income – along with greater sustainability – of developing countries’ firms participating in GVCs;

- economic and social upgrading – see for example the role of GVCs in female employment and the promotion of gender equality (Staritz and Reis, 2013);

- diffusion of technology and knowledge – see for example the proportion of foreign patent filing (World Intellectual Property Organization, WIPO), or the training efforts made by lead firms established or subcontracting in developing countries;

- political and economic stability – see for example the proportion of firms involved in GVCs that participate in the UN Global Compact (unglobalcompact.org).

Beyond anecdotal evidence and case studies, either at the firm or industry level, hard data exist on employment (e.g., ILO) and FDI (e.g., UNCTAD) that could be analyzed at the industry level and matched with the measurement of participation in GVCs.

A number of GVC analyses also attempt to map “who gets what” in the value chain, often at the micro (firm or product) level. For example, sourcemap.com attempts to map all the GVCs to offer consumers greater transparency on the origin of products.

# **PART 3 – INCREASING TRADE INTEGRATION THROUGH GVCs**

The ability of a country to participate in global trade and benefit from the transfers that will generate growth and development is now partially linked to its ability to join major GVCs. Competitiveness is not measured in terms of a country’s capacity to develop an integrated industry, but its capacity to identify its best position in GVCs. It is not about picking the winners, but choosing races and placing bets (Greenaway, 2012); national champions could provide very specific tasks at key points of
age.<sup>27</sup>

# **3.3 Descriptive Statistics and Balancing Tests**

Table 1 shows descriptive statistics of socio-demographic information for the full sample and separated by treatment arm. Panel A presents summary statistics of characteristics available for the full sample. On average, caregivers in our sample are 31 years of age,<sup>28</sup> 52 percent are women, and have an oldest child who is 4.6 years old. In terms of phone exclusivity, only 23 percent of participants share a phone with other relatives within the household. Finally, 3 of each 10 participants use a money-transfer technology (Tigo Money). Only 60 and 44 percent of caregivers completed baseline and endline surveys, respectively.

Panel B in Table 1 shows descriptive statistics for variables that are available only for individuals who completed the baseline survey. We find that most caregivers (64 percent) in our sample do not have tertiary education (bachelor’s degree or higher). We also show that 28 percent of the sample were either unemployed or lost their jobs during the pandemic.<sup>29</sup> Both treatment arms are balanced in most socio-demographic characteristics except for gender; there are more women in the WhatsApp (self-completion) group.<sup>30</sup> Since our sample involves participants with and without a baseline survey completed we also test for potential selection in pre-determined characteristics taking into account this feature. These results are presented in Figure A2 and show that there are no significant differences across arms.

Table 2 presents summary statistics of our main outcomes. In panel A, we show that on average, the completion rate was 51 percent and the number of attempts to reach participants (reminders) was greater than three. Moreover, between 60 and 68 percent of participants in our study were consistent in their responses of sensitive information at baseline, and between 58 and 62 percent at endline. Among reasons for

> 27In Table A10 in the Appendix,
Report on Foreign Trade Barriers The 2005 National Trade Estimate Report on Foreign Trade Barriers (NTE) is the twentieth in an annual series that surveys significant foreign barriers to U.S. exports. The report provides, where feasible, quantitative estimates of the impact of these foreign practices on the value of U.S. exports. Information is also included on actions taken to eliminate barriers.

The NTE provides an account of barriers and unfair trade practices to American exports of goods, services, and farm products. Besides limiting opportunities for U.S. businesses and farmers, such barriers also undermine the benefits that foreign countries, particularly developing countries, see from trade liberalization. The NTE covers 61 major trading partners in each region of the world and profiles policies restricting market access.

# _<u>Protection Effect Survey</u>_

Ronald Fischer at University of Chile conducted in-depth interviews of 15 Chilean firms in terms of non-tariff barriers. The dataset includes a set of protectionist measures such as administrative measures, invasive inspection of containers, the misuse of phitosanitary and other standards for protection, etc..

14 __________________________________________________________________________________________________________
Using panel data for 95 economies between 1990 and 2012, we find that the presence of domestic violence legislation does indeed lead to lower women-to-men adult mortality rates. The results are robust to various factors, including overall economic development, quality of health care provision, prevalence of diseases, the broader institutional and legal environment, as well as omitted variables that do not vary over time or across countries as captured by country and year fixed effects. We also exploit the role of human rights conventions in generating legislation to instrument for the presence of domestic violence legislation. Our main findings are confirmed with the Instrumental Variables (IV) approach. Based on our findings, millions of women could have been saved annually since 1990 had domestic violence legislation been implemented.

In the broader literature, several studies have explored the relationship between factors that raise women’s status and women-specific outcomes. Some studies have explored the effect of women’s employment and education on abuse suffered from their husband and distribution of resources within the household (Bowlus and Seitz, 2006; Vyas and Watts, 2009). Studies have also explored the effect of women’s status in terms of political agency, education, and labor force participation on infant mortality rates (Bhalotra and Clots-Figueras, 2014; Gakidou et al., 2010; Zakir and Winnava, 1999).

The remainder of the paper is organized as follows: Section 2 presents the conceptual framework, section 3 presents the data and main variables, Section 4 presents the baseline empirical results, section 5 presents the Instrumental Variables approach, section 6 provides the magnitude of the effects, while section 7 concludes.

4