rafmacalaba/data-use-mentions-extended huggingface dataset:
Countries with good institutions rarely default. Figures 1 and 2 show the negative relationship between the two measures of institutional quality and sovereign default probability. The correlations of sovereign default probability with the ICRG institutional index and regulation of participation are -0.53 and -0.44, respectively. They are significant at 1%. Columns 1 and 2 of Table 3 report the summary statistics of institutional quality and government polarization of nondefaulters, one-time defaulters and serial defaulters. Non-defaulters, countries that have not defaulted during this period, have a substantially higher index in both measures of institutions than defaulters, and the difference is statistically significant at 1%. However there is no statistical difference between one-time defaulters and serial defaulters.

Column 3 of Table 3 shows the average government polarization. Non-defaulters on average have higher polarization than defaulters; and among defaulters, one-time defaulters are less polarized than serial defaulters. However, these differences are not statistically significant. This is also shown in Figure 3. It seems that default probability is not positively correlated with government polarization. In fact, the pairwise correlation between default probability and government polarization is 0.01 and it is not significant. I will argue that polarization does not matter under the presence of good institutions because powerful groups, regardless of how polarized they are, cannot influence government decisions. Therefore countries with more polarized government do not necessarily default more times. To show this, I perform an OLS analysis where the dependent variable is the default probability. I first show that quality of institutions is an important determinant. Second, conditional on having weak institutions, default probability is higher in countries with a higher degree of government polarization.

It could be argued that there might be omitted variables that could explain cross-country differences, in both institutional quality and default frequencies; for example, stage of development. This is a valid argument since most advanced economies enjoy better institutions and rarely default. The correlation between real GDP per capita and default probability is -0.46, significant at 1%. Another
Table 1: Procurement and budget execution coverage

|State|#||Pr|ocurement|||Budget Exe|cution||
|---|---|---|---|---|---|---|---|---|---|
||Munic.|Tender|Tender|Tender|Temporal|Commitment|Verifcation|Payments|Temporal|
||||Item|Participants|Coverage||||Coverage|
|CE|184|✓|✓|✓|2009-2021|✓|✓|✓|2009-2021|
|MG|853|✓|✓|✓|2014-2021|✓|✓|✓|2014-2021|
|PB|223|✓||✓|2014-2020|✓|✓|✓|2003-2021|
|PE|185|✓||✓|2012-2021|✓|✓|✓|2012-2020|
|PR|399|✓|✓|✓|2013-2021|✓|✓|✓|2013-2021|
|RS|497|✓|✓|✓|2016-2021|✓|✓|✓|2010-2021|
|SP|644|||||✓|✓|✓|2008-2021|
|Total|3,076||||2009-2021||||2008 - 2021|

**Notes** : This table reports temporal and geographical coverage of our dataset. For procurement data, the number of municipalities for PR is 392 due to problems with the conversion of xml files. In the budget execution data, the original data does not include the municipalities of _Quixabá_ and _Santa Teresinha_ in the state of PB. We could not obtain the data for the São Paulo municipality (state capital), which is supervised by a separate audit court.

Table 2: Descriptive statistics - public procurement

||CE|MG|PB|PE|PR|RS|Total|
|---|---
|villages in the Sahel<br>(Aker and Jack 2023)|deviations compared to<br>the control group|uncultivable land had<br>been restored.||
|---|---|---|---|
|No-till and winter cover<br>crops for winter wheat,|The gross profit margin<br>outperformed||SOC, measured at 0–<br>30 cm depth, increased|
|maize, and soybean, in|conventional tillage by||by 2.75% annually|
|Piacenza, Po Valley,|6% to 200% over 2011||from 2011 to 2020 and|
|Northern Italy|to 2020.||didn’t vary by the type|
|(Lorenzetti and Fiorini<br>2024)|||of cover crop.|

# **<mark>3.1 On Productivity</mark>**

Research has shown that certain regenerative agricultural practices or their combination can enhance crop productivity, particularly in dry climates. A global meta-analysis involving 610 studies found that employing no-till alongside residue retention and crop rotation significantly boosts rainfed crop productivity in dry climates (Pittelkow, et al. 2015). Additionally, rainwater harvesting techniques have demonstrated a notable increase in the value of agricultural production, ranging from 0.12 to 0.15 standard deviations compared to the control group in the Sahel (Aker and Jack 2023). In Malawi and Zimbabwe, the adoption of practices such as crop residue utilization, crop rotation, and minimum tillage has been associated with a substantial 12%-18% increase in cereal productivity (Makate, et al. 2019). In addition, some evidence suggests that a combination of regenerative agricultural practices can create a larger impact on productivity gains than being adopted in isolation (Tambo and Mockshell 2018).

However, most of the literature only focuses on the benefits of adopting regenerative agricultural practices, such as an increase in yield
This illustrates the trade-off between collecting cross-sectional data for the purpose of single-difference matching, versus collecting longitudinal data with a lighter survey instrument. An important factor in deciding which method to use is how much we know _ex ante_ about the determinants of program placement. If a single survey can convincingly capture these determinants then PSM will work well; if not then one is well advised to do at least two rounds of data collection and use DD, possibly combined with PSM, as discussed below.

While panel data are not essential for estimating _DD_ , household-level panel data open up further options for the counterfactual analysis of the joint distribution of outcomes over time for the purpose of understanding the impacts on poverty dynamics. This approach is developed in Ravallion et al. (1995) for the purpose of measuring the impacts of changes in social spending on the inter-temporal joint distribution of income. Instead of only measuring the impact on poverty (the marginal distribution of income) the authors distinguish impacts on the number of people who escape poverty over time (the “promotion” role of a safety net) from impacts on the number who fall into poverty (the “protection” role). Ravallion et al. apply this approach to an assessment of the impact on poverty transitions of reforms in Hungary’s social safety net. Other examples can be found in Lokshin and Ravallion (2000) (on the impacts of changes in Russia’s safety net during an economy-wide financial crisis), Gaiha and Imai (2002) (on the Employment Guarantee Scheme in the Indian state of Maharashtra) and van de Walle (2004) (on assessing the performance of Vietnam’s safety net in dealing with income shocks).

Panel data also facilitate the use of dynamic regression estimators for the _DD_ . An example of this approach can be found in Jalan and Ravallion (2002), who identified the effects of lagged infrastructure endowments in a dynamic model of consumption growth using a sixyear household panel data set. Their econometric specification is an example of the nonstationary
the formal sector: the bulk of these expenditures are utilities typically provided by large institutions which cannot evade taxes (Figure A3). Appendix B provides more details on the methodology. Table B4 shows for each country the original names of the places of purchase, their expenditure shares and our formality assignment.

We will investigate the robustness of our results to two alternative formality assignment rules. The first alternative is to assign a non-binary probability to each category that it pays VAT on its sales. We use the share of retailers that pay VAT in each category in the Mexican census to obtain a for each These are probability category. probabilities depicted in Panel C of Figure 1.<sup>12</sup> This enables us to take into account the fact that some small retailers pay taxes – either because a subset of small retailers are fully formal or because all small retailers pay VAT on a subset of their transactions. The second alternative changes specialized stores (category 4) to be fully informal (leaving all other categories unchanged). This is the category for which there is arguably the most uncertainty about its true formality status.

Our formality assignment rule enables us to measure the informal budget share of each household, defined as the share of its total expenditure purchased from informal retailers. In what follows we also consider within-product informal budget shares. We use the UN’s detailed COICOP classification of products, which is available at the 2-digit level (12 products), 3-digit level (47 products) and 4-digit level (117 products).<sup>13</sup>

# **2.3 Extended Sample for Food Expenditures**

We complement our core dataset with microdata from the World Bank’s Global Consumption Database (GCD). The GCD compiles household expenditure surveys across countries and harmonizes product categories across all surveys at the 2-digit COICOP product level. 14 Merging this dataset with our core sample, we obtain consumption data for 89 low and middle income countries which account for over 50% of the
# 1 Introduction

Standard models in many ...elds of economics posit the existence of a representative household in either a static or a dynamic setting, and then seek to relate the welfare of that household to observable aggregate data. A separate large literature examines the productivity residual de...ned by Solow (1957), and interprets it as a measure of technical change or policy e¤ectiveness. Yet a third literature, often termed "development accounting," studies productivity di¤erences across countries, and interprets them as measures of technology gaps or institutional quality. To our knowledge, no one has suggested that these three literatures are intimately related. We show that they are. We start from the standard framework of a representative household that maximizes intertemporal welfare over an in...nite horizon, and use it to derive methods for comparing economic well-being over time and across countries. Our results show that under a wide range of assumptions, welfare can be measured using just two variables, productivity and capital accumulation. We take our framework to the data, and measure welfare change within countries and welfare di¤erences across countries.

In the simplest case of a closed economy with no distortionary taxes we show that to a ...rst-order approximation the welfare change of a representative household can be fully characterized by three objects: the expected present discounted value of total factor productivity (TFP) growth as de...ned by Solow, the change in expectations of the same quantity, and the growth in the stock of capital per person. The result sounds similar to one that is often proven in the context of a competitive optimal growth model, which might lead one to ask what assumptions on technology and product market competition are required to obtain this result. The answer is, None. The result holds for all types of technology and market behavior, as long as consumers take prices as given and are not constrained in the amount they can buy or sell at those prices. Thus, for example
Dhaka and showed that the first lockdown came with much higher costs than the subsequent ones. The smaller economic impacts of the later lockdowns can partly be explained by the nature of the lockdowns. For example, while all of them seem to have been similarly stringent according to the OxCGRT Stringency Index, factories were only shut during the first lockdown. In addition,

> 11 The Google mobility trends baseline period is defined as the median value for the corresponding day of the week, during the five-week period from January 3 to February 6, 2020.

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# Table (F4) Impacts of the Program on Financial Participation

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Has savings_≥_30 JD|0.01|(0.04)|[1.00]|0.09|(0.28)|1,321|
|Lent informally last month {0,1}|0.03|(0.02)|[1.00]|0.02|(0.14)|1,321|
|Lent informally last month (JD)|0.16|(0.53)|[1.00]|0.43|(3.48)|1,321|
|Borrowed informally last month {0,1}|-0.00|(0.06)|[1.00]|0.72|(0.45)|1,321|
|Borrowed informally last month (JD)|-34.49|(46.00)|[1.00]|229.83|(359.22)|1,318|
|Borrowed or lent formally last year {0,1}|0.02|(0.04)|[1.00]|0.07|(0.25)|1,321|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- Has savings _≥_
the main criticisms of the adoption strategy is that placement of adopted children might not be random: children left for adoption are placed in families with characteristics similar to their biological families. This is, of course, especially true in the case of adoption by relatives. The rich information contained in the EICV allows us to control for non-random placement and thus avoid this criticism.

In the main specification, the results indicate that, even after controlling for the schooling of the biological parents and the type of relationship linking the child and the head of his new household, the education of the most educated female adult in the new household has a positive and significant effect on the schooling of the child welcomed in the household. The magnitude of the effect is similar to the effect in a biological mother-child relationship. The effect of the education of the most educated male in the relationship is smaller than in a biological father-child relationship, but remains positive and significant. These results suggest that the nurture component of the intergenerational transmission of human capital is important, in particular for mothers, contrary to the evidence proposed by Behrman and Rosenzweig (2002) and Plug (2004). The analysis of interaction terms, however, indicates that these positive effects are only present for children related to the head of their new household (grandchildren and other relatives). When boys and girls are analyzed separately, it appears that, when the nurture component is isolated, the mother’s education matters more for girls, while the father’s education has a stronger effect on boy’s educational achievement.

> 2 To the best of my knowledge, only Björklund, Lindahl and Plug (2004) also use a data set containing information about the characteristics of the biological parents.

4
size remains relatively stable in the agriculture and services sectors and exhibits cyclical behavior in the manufacturing sector.<sup>4</sup>

> 4The evidence on the relationship between a country’s income per capita level and average firm size is not conclusive. For example, using data on the manufacturing sector, Alfaro et al. (2009) find that firm size decreases with the level of income across nations, and Laincz and Peretto (2006) report no trend in average firm employment in the U.S. More recent papers, such as Poschke (2017) and Bento and Restuccia (2017), on the contrary, document that average establishment size is positively correlated with GDP per capita in the cross-section of countries.

4
# 3.8. Data for the approximation of economic impacts

This section describes non-spatial aggregate statistics used to convert exposure shares into economic figures in the sectors tourism, aquaculture, and agriculture.

To approximate the economic impact of hazards on the tourism sector, macroeconomic estimates were sourced from World Bank open data<sup>4</sup> and publications. National tourism GDP was obtained by applying the contribution of tourism to GDP in 2017 (WTTC, 2018) to the national GDP in 2017 (World Bank, 2019). The number of direct jobs in the tourism industry in 2017 are obtained from numbers published by the World Travel and Tourism Council (WTTC, 2018).

In the fisheries sector, aquaculture output in tons per province in 2017 was sourced from the General Statistics Office of Vietnam (GSO, 2019). Total country export value of aquaculture production for 2017 (Danh, 2016; VietNam News, 2019) has been estimated after applying a 65 percent contribution of aquaculture to the total fisheries sector 2020 (Dinh, 2017a). The number of jobs in the aquaculture sector is estimated using the number of jobs created per ton of output from the National Fisheries Development Strategy to 2020 (Dinh, 2017a). Assuming no significant change in productivity from the 2020 outlook in 2017, this number has been used to estimate the amount of jobs in the aquaculture sector using actual production for 2017.

Vietnam’s combined agriculture, forestry and fishing GDP for 2017 and the combined employment in agriculture (that is, from agriculture, forestry and fishery) for 2017 has been sourced from World Bank macroeconomic indicators (World Bank, n.d.). To estimate the contribution of the cultivated crops sector to the combined agriculture-forestry-fisheries sector, contributions of these individual sectors to the combined agriculture sector have been sourced from different World Bank publications (Dinh, 2017a, 2017b, 2017c; World Bank, 2010). For aggregated cultivated crops the provincial area used for cultivation is derived from the researchers’ own calculations,
||Targeted allowance for house readjustment|X|||
|---|---|---|---|---|
|Family and|Monthlybenefit for bringingupa child upto oneyear old.||X||
|Children|Monthly allowance for a child up to the completion of secondary education but no<br>more than 20years of age||X||
||Lumpsum benefit forpregnancy||X||
||Targeted allowance for schoolchildren enrolled in 1st grade at a state or municipal<br>school(income-tested before 2021)||X||
||Monthly benefit for bringing up children accommodated in families of relatives and<br>friends.|||X|
|Social|MSA Monthlysocial allowance(MSA)|||X|
|exclusion|Heatingallowance|||X|
|Education|Scholarshipforpupils||X||
||Scholarshipfor students,PhD students,andpost-graduate students||X||

Source: ESSPROS database, OECD (2020), Tosheva et al. (2022)

**The Guaranteed Minimum Income (GMI) Indicator was determined by the Council of Ministers until mid-2023 and lacked anchoring in any national or international poverty, consumption, or income measure indicative of an adequate living standard, indicating a discretionary indexation** . The level set was notably low and was not updated for years, staying at BGN 75 (EUR 38) – leading to insufficient coverage and adequacy of the Monthly Social Allowance scheme, heating allowance, and other associated social payments. Beyond the low benefit level, the GMI design failed to adapt or link social assistance to fluctuations in purchasing power and general living standards, as it remained unadjusted and unindexed for inflation. Consequently, this has resulted in diminishing benefit adequacy over time, with GMI indicators as a percentage of the AROP poverty line decreasing from 21.4 percent in 2016 to 14.3% in 2021 (Figure 3). Given this, the design of the
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|Economic evaluation|1997|Pulling Cost-Effectiveness Analysis Up by Its<br>Bootstraps: A Non-parametric Approach to<br>Confidence Interval Estimation|Andrew Briggs|Health Economics|326|163|
|Economic evaluation|2001|Representing Uncertainty: The Role of Cost-<br>Effectiveness Acceptability Curves|Elisabeth Fenwick|Health Economics|321|167|
|Economic evaluation|1999|<br>Determining Clinically Important Differences in<br>Health Status Measures: A General Approach<br>with Illustration to the Health Utilities Index<br>Mark II|Greg Samsa|PharmacoEconomics|316|170|
|Economic evaluation|2000|<br>A Comparative Review of Generic Quality-of-<br>Life Instruments|Stephen Joel Coons|PharmacoEconomics|308|179|
|Economic evaluation|1997|<br>Accounting for Future Costs in Medical Cost-<br>Effectiveness Analysis|David O. Meltzer|Journal of Health Economics|308|179|
|Economic evaluation|1998|<br>An Introduction to Markov Modelling for<br>Economic Evaluation|Andrew Briggs|PharmacoEconomics|307|183|
|Economic evaluation|1976|<br>The Economic Cost of Illness Revisited|Barbara S. Cooper|Social Security Bulletin|305|187|
|Economic evaluation|2004|Does NICE Have a Cost-Effectiveness Threshold<br>and What Other Factors Influence Its Decisions?<br>A Binary Choice Analysis|Nancy Devlin|Health Economics|302|191|
|Economic evaluation|1999|<br>The Irrelevance of Inference: A Decision-Making<br>Approach to the Stochastic Evaluation of Health<br>Care Technologies|Karl Claxton|Journal of Health Economics|300|195|
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gross domestic credit; SME = small and medium-size enterprises.

All the institutional variables are expressed as scores. These scores capture the gap from the best performance ever attained for a given indicator since the World Bank started compiling the Ease of Doing Business (EADB) survey. Therefore, each of the institutional variables takes values from 0 to 100—with 100 being the best performance. The Ease of Doing Business represents an overall indicator of pro-business regulation and the protection of property rights. Enforcing contracts is a sub-index of EADB that measures the time and cost for resolving business disputes in local courts, as well as the overall quality of judicial processes. Getting Credit is an EADB sub-index that examines two aspects of the regulatory framework for access to external sources of financing: the strength of credit reporting systems and the effectiveness of bankruptcy and collateral laws. Tables A.3 to A.5, in the appendix, contain the correlations among the explanatory variables.

Additionally, we include the pre-treatment level of the dependent variable in all our regressions and label this variable as _Initial conditions._ In doing so, we appreciate the role of initial conditions, which have been shown to be a determining factor in firms’ outcomes in emerging markets (Ayyagari, Demirgüç-Kunt, and Maksimovic 2017).

14
WHO 2007), such as working with same-sex enumerators, providing specialized training to enumerators, developing protocol for potential interruptions, and providing information on support/referral services for GBV survivors. We received research ethics review approval from Health Media Labs IRB.

Further, we complemented the in-person data collection with a phone survey in April-May 2020 to collect appropriate data on receipt of the 4P benefits: in our initial round of data collection the question about receipt of 4P benefits was addressed to the respondent of the household questionnaire (i.e., the most knowledgeable person in the household), which resulted in high number of missing values and inconsistencies. To bridge this data gap and resolve data irregularities, the phone survey, which included a limited set of questions, was administered to the _target_ respondents.

4.1 Primary Outcome of Interest Violence against women is the primary outcome of interest. We record data on 12 types of violence, following 2 types of classification. The first classification categorizes violence by form of violence, namely

> 10 Results available upon request.

9