rafmacalaba/data-use-mentions-extended huggingface dataset:
public sector to create _effective_ services. Second, the impact of the provision of public services will depend on choices and the market for health, that is both on the private demand and how it may vary across disease conditions, and private supply and how it responds to public intervention.

# _III) Public sector spending_ **_and_** _the creation of effective health services_

One reason why PHC might have little impact on health status is not that _in principle_ PHC is unimportant, but that _in practice_ the efficacy of government actions has been low. Without personal experience it is difficult to appreciate how bad "low quality" public sector services can be. Traveler's "tales" of public health clinics are legendary and we'll give just three anecdotal examples. First, while one of the authors was visiting one low income country a prominent newspaper accused the Ministry of Health of misappropriating _$50 million_ of donor financing. The ministry the next day accused the newspaper of exaggeration and irresponsibility for failing to make it clear that this $50 million was misappropriated over a period of _three_ years, not in a single year as the newspaper reports implied. Second, a client survey of women who had a birth in the past 2 years at rural health centers in the Mutasa district of Tanzania revealed the most frequently cited disadvantages of giving birth in an institution were: _ridiculed by nurses_ for not having baby clothes (22 percent), maternity fees (16 percent), nurses ordered mothers to wash linen used soon after delivery (16 percent), and _nurses hit mothers during delivery!_ (13 percent) (Mtemeli, 1994). ' Third, in nearly every '' Interestingly, when nurses themselves were asked to give reasons why they thought mothers did not deliver in health institutions, the most frequently cited reasons were: 19
# **3 Administrative Data**

We use administrative data from January 2018 through December 2023, compiled through an ongoing collaboration with the local revenue agency in Tres de Febrero.

**Local property tax data.** The primary database consists of monthly property tax records constructed from the monthly bills issued to account holders. The unit of observation is an account that coincides with a dwelling unit. The data contain the following billing details: account number (unique property identifier), address, name of locality (neighborhood), year and month of the bill (12 yearly bills), the monthly fee (in pesos), a payment indicator, due date, date of payment, days overdue, means of payment (cash or electronic), type of account (residential, retailer, manufacturer), and also information about property size.

**Property ownership, assessed value, and gender.** Tres de Febrero receives a yearly data file from the Province of Buenos Aires containing the assessed values of local properties as well as individual tax identifiers of up to two owners. The authorities merge this register with their tax data using the cadastral nomenclature.<sup>6</sup> Crucially, there is a way to infer the sex assigned at birth and the age of the owners based on the first two digits and the last digit of the individual tax identifier (as well as from owners’ names). Appendix B provides a detailed description.

**RCT data.** We have access to treatment assignments from a large-scale randomized communication campaign conducted by Cruces et al. (2023), who estimate direct and spillover effects on property tax compliance. The campaign consisted of sending 25,000 personalized letters to randomly selected properties in October 2020 with reminders about due taxes, information about the status of the account, due dates, past due debt, and payment methods. We combine this database with the previous two sources to investigate heterogeneous behavioral responses to nudges between men and women and
covariates and pairs green and non-green contracts achieving a high degree of similarity across the two groups and hence minimizing confounding due to observed covariates (Iacus et al, 2012). The covariates used for matching are

- Buyer type (e.g., central government body, regional/local entity)

- Contract type (goods/works/services)

- Product market (2-digit CPV code)

- Buyer location (NUTS3)

- Supplier location (NUTS3)

- Tender year.

In the second step, we run regression models on the matched samples, including control variables:

- Contract value (categorized as deciles)

- Buyer size: total value of contracts awarded annually.

Individually and taken together, these control variables account for observable contract and organization characteristics that are likely to impact public procurement outcomes such as competition and corruption risks, but also on the use of GPP. For example, green contracts may be more frequent in larger markets where suppliers may also face more bidders. Such market characteristics are accounted for by including buyer and supplier location, buyer size and product market category. Similarly, larger tenders may be more amenable to green procurement as they are able to absorb higher investment costs of using new technologies. This is controlled for by including contract value in the regressions.

The comprehensive information on public procurement transaction data in Bulgaria is thus critical to identify the impact of green public procurement on procurement and firm outcomes.

The second modeling approach tackling challenges to causal identification we deployed is interacted fixed effects - market * year - regressions on the contract level, including the same set of control variables as for matching. The extensive set of fixed effects are designed to capture a wide range of unobserved variables, in addition to controlling for observable confounders. In essence, the fixed effects regressions allow us to identify the impacts of GPP in very narrowly defined spaces, within any particular combination of market-years. By implication, our impact estimates should be interpreted as within market changes while also controlling for any time-dependent external shocks. Looking at
COVID-19 shock. We rely on BPS for Afghanistan, Bangladesh, India, Nepal, Pakistan, and Sri Lanka, which includes information that spans from the onset of the crisis and the implementation of lockdown measures to the recovery phase, where mostly all firms were fully open.<sup>1</sup> _Second_ , we construct a composite measure of informality that accounts for its multidimensionality through the continuous index based on a Tetrachoric Principal Component Analysis (PCA). This approach allows us to measure the degree of informality on a continuum. _Third_ , we distinguish between low-productivity informal firms born out of “necessity” versus those that are comparable to formal firms but they still choose to remain informal to evade costs associated with formality (“parasite” firms).<sup>2</sup> _Fourth_ , we separate the role of external attributes and internal characteristics in correlating the effect of the crisis on informal firms.

The remainder of the paper is structured as follows: Section 2 summarizes the literature on the definition and measurement of the informal sector focused on firm informality. In Section 3, we explain the methodological aspects of the analysis, including the unique characteristics of the Business Pulse Survey dataset, the structure of the informality composite index that we propose, and our analysis of measures that differentiate across different views of informality such as necessity, De Soto, and “parasite” firms (See Ulyssea, 2018, 2020). We also highlight the identification strategy. Section 4 presents descriptive analyses of the variables of interest in the sample. Section 5 presents the main results of our analysis of the heterogeneous effects of the crisis according to informality, while Section 6 concludes.

# **2. Related Literature**

# **2.1 Definition and Measurement of Informality**

Informality may include definitions from the perspective of the workers, firms, and government. These may be inter-related but have different implications. On the workers’ side, a job that does not provide standard labor protection falls under informality
>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|160|160|160|101|101|101|95|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|1083<br>05, *** p<0.<br>(WDI), Open|1083<br>01. Standard er<br>Data Watch (|1083<br>rors are cluster<br>ODIN), Global|362<br>ed at the count<br>Data Baromete|362<br>ry level. Data<br>r (GDB), and|362<br>from the Worl<br>Open Data Ba|95<br>d Bank's World<br>rometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

68
selected youth. γ is the difference in

> 9 Due to some procurement delays that caused a big time-lag between baseline data collection and actual NGO project implementation, many of the volunteers who belonged to selected NGOs and who were randomly selected to participate in the impact evaluation study dropped out after their baseline data were collected and were replaced by other volunteers. Project monitoring data reveal that 23 percent of volunteers assigned to treatment did not actually end up participating in the NVSP. Given the relatively high number of non-compliance, we are unable to perform Local Average Treatment Effects (LATE) analyses to understand the impact of participating in NVSP on outcomes of interest.

9
robustness to the assumptions underlying the various estimators. We find that regardless of the technique employed, there is a strong relationship between R&D effort at the global level and innovation with a long run elasticity surprisingly close to 1.

We contribute to the understanding of the dynamics of knowledge creation. Hall, Griliches and Hausman (1986) argue that, at the firm level, patenting occurs virtually

> 6 One strand of the empirical literature working broadly in the growth tradition has measured the returns to R&D using both micro and cross-country data.<sup>6</sup>

3
(NSO) of Malawi in order to match the boundaries with the census data at the enumeration area level. Malawi has 12,666 enumeration area boundaries from the 2008 census. The IHS3 household survey has 12,271 observations from 768 enumeration areas.<sup>6</sup>

> 6See the background report for more details on the design, sample and survey results at: http://microdata.worldbank.org/index.php/catalog/2939/download/41224

15
Roubaud, 2009, Education and earnings in urban West Africa, Journal of Comparative Economics 37, 491‐515 Martin, E., and A.E. Polivka. 1995. “Diagnostics for Redesigning Survey Questionnaires: Measuring Work in the Current Population Survey.” _Public Opinion Quarterly_ 59: 547‐567.

Mata Greenwood, A. 2000. _Incorporating Gender Issues in Labour Statistics_ . Geneva: International Labour Office, Bureau of Statistics.

Mathiowetz, N.A., and R.M. Groves. 1985. “The Effects of Respondent Rules on Health Survey Reports.” _American Journal of Public Health_ 75(6): 639‐644.

McCabe, J. L. And M.R. Rosenzweig, 1976, Female labor‐force participation, occupational choice, and fertility in developing countries, Journal of Development Economics, Elsevier, vol. 3(2), pages 141‐160 Meghir, C. and S. Rivkin. 2010. “Econometric Methods for Research in Education.” Institute for Fiscal Studies Working Paper W10/10 Mincer, Jacob. 1958. “Investment in Human Capital and Personal Income Distribution.” _Journal of Political Economy_ 66(4): 281‐302 24
including also non‐complying households. We control for all stratification and re‐randomization characteristics. Detailed estimation results can be found in the supplemental appendix.

_Source:_ Household data set 2011/2012.

Altogether, we observe an effect of Pico‐PV kit ownership on time dedicated to domestic activities only in the case of study time of primary school boys. For other Pico‐PV users, we only observe a higher flexibility in organizing their domestic duties. Moreover, it is plausible to expect that the improved lighting also increases the effectiveness of the tasks it is used for. Quantifying this effect is beyond the scope of our study, though. At least for the case of students’ school test‐scores the Furukawa (2014) results advise some caution in making hasty statements about improving learning outcomes based on longer study times and better light.<sup>16</sup>

# **Conclusion**

Our results show that simple but quality verified Pico‐PV kits in fact constitute an improvement compared to the baseline energy sources, mostly dry‐cell batteries and kerosene. Given the small size of the panel, the charging capacity is obviously not abundantly available, and many households did not manage to use the panel for charging the radio and mobile phones; lighting turned out to be the most often used

> 16 Remember that Furukawa (2014) observes even a decline of school children’s test scores in spite of an increase in study time in an RCT using solar lanterns in Uganda. One explanation for this puzzling outcome he provides is the potentially bad lighting quality of the solar lamp. In our case, we have no indication for such a bad lighting quality and we believe this would have been disclosed in the various qualitative interviews we conducted (in which many other problems were discussed pretty openly, see Section IV).
# **Introduction**

Iraq was plunged into two simultaneous crises in the second half of 2014, one driven by a sharp decline in oil prices, the other, by the war against the Islamic State. Since June 2014, crude oil prices per barrel have fallen from around 112 USD to 97 USD in September and 62 USD by December (Figure 1). Given Iraq’s heavy dependence on oil as a share of GDP and exports, and a source of government revenues, this decline in prices alone would have hit Iraq’s fragile economy hard.

In addition, since June 2014, Islamic State (IS) or Da’ash militants extended their influence from Syria into Iraq’s northern and western provinces of Anbar, Nineveh, Salahadin, and to a lesser extent, Kirkuk and Diyala. A total of 354,000 families were internally displaced between June and December of 2014 which represents about 2.1 million individuals;<sup>3</sup> and those left behind have been cut off from the rest of the country. The internally displaced persons (IDPs) have sought refuge across Iraq and about half of those who have crossed governorate boundaries were settled in Iraqi Kurdistan.<sup>4</sup> Civilian casualties in Iraq have increased to close to 2007 levels (Iraq Body Count) as a result of the ongoing violence (Figure 2). The prevailing insecurity has severely affected oil exports from the north, adversely impacted trade and investment, led to the destruction of infrastructure and impeded the flow of goods and services across the country, leading to a sharp contraction in oil and non‐oil GDP (World Bank, Iraq Macro‐Poverty Outlook (Internal Use Only), Spring 2015).

The twin crises are likely to have impacted households across Iraq through different transmission channels. Even for households directly affected by the war against IS, the welfare of those displaced, their hosting communities, and those left behind will all be affected differently.

On one
WPS3737

# **Global Antidumping Database Version 1.0**

## **Chad P. Bown**<sup>**†,‡**</sup>

## **Brandeis University**

## **Abstract**

This paper describes a newly collected, detailed database on national governments’ use of the antidumping trade policy instrument. The data collection project was funded by the Development Research Group of the World Bank and Brandeis University. While still preliminary, it goes beyond existing, publicly-used sets of antidumping data in a number of fundamental ways. It is a first attempt to use original source national government documentation to organize information on products, firms, the investigative procedure and outcomes of the historical use (since the 1980s) of the antidumping policy instrument across large importing country users. We also report more and recent data on a number of smaller users of antidumping, as well as some limited information on the use of countervailing measures from national governments that are users of countervailing duty laws.

### **Database Website:** **<u>http://www.brandeis.edu/~cbown/global_ad/</u>**

World Bank Policy Research Working Paper 3737, October 2005 _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, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ ____________________

> **†** Department of Economics and International Business School, Brandeis University, MS 021, PO
The economic downturn arising from the measures to contain the outbreak will not only affect the poor but may also propel large numbers of people into poverty. Experience suggests that unemployment rates can rise sharply during such a global crisis. In Kosovo, the supply and demand shocks to the economy arising from the containment measures adopted by the government are expected to reduce household earnings and increase the already large pool of untapped labor, which may moderate wage pressures further.

# **Several policy recommendations arise from this analysis:**

**First, the analysis shows the importance of producing high-quality labor market data.** The analysis provided in this note is limited to the LFS which does not provide exact wage data. Measurement of wages in the LFS should be improved and complimented by other surveys and administrative data. To monitor labor market response to minimum wages in a robust way, a matched employer-employee panel data set needs to be built (see <mark>Portugal and Cardoso [2006] for a discussion of the potential use of matched employer-employee data for the analysis of the impact of minimum wages). Data limitations not only prevent us from determining a precise estimate of the minimum wage impact but also from estimating the wage distribution. Precise estimates of the median and minimum wages are critical for setting the minimum wage because they allow us to evaluate the extent to which the minimum wage is binding.</mark>

**Second, monitoring the labor market is essential, as well as accurately estimating the wage elasticity of labor demand.** The minimum wage can have different effects depending on the context of the country at a certain point in time. Therefore, in order to set a minimum wage and to impose an adjustment formula, a detailed monitoring of the labor market and the estimation of the key parameters which determine the effect of the minimum wage are needed. In this note, we consider several scenarios based on the estimates of the wage elasticity of labor demand from international evidence, which might not apply for Kosovo
16 economy‘. None of the most cited items in ̳Public health‘ exceeds 1,000 citations but it is led by two classic applied papers: Becker, Grossman and Murphy (1994) on cigarette addiction (placed here rather than ̳Determinants‘ on account of its emphasis on smoking reduction), and Cutler, Glaeser and Shapiro (2003) on American obesity. This category also includes economic analyses of risk factors, heat waves, AIDS and violence. ̳Health and the economy‘ is led by Rodrik (2004) on economic development and DiMasi et al. (2003) on pharmaceutical innovation. The other papers explore further the two-way links between health (or health care) and the wider economy: growth, the economic cycle, unemployment, and private and public expenditure.

̳Supply of health services‘ has a historically characteristic focus on hospitals as nonprofit institutions, managed health care organizations and the ways in which such institutions respond to changes in their financial environments. None of the items in this category reach the rates of citation of the most cited papers. The most cited paper in this group (Cleary and McNeil, 1988) was 32<sup>nd</sup> in the overall ranking, the next most highly cited paper was 40<sup>th</sup> (Scott and et al., 2000). Newhouse‘s (1970) pioneering paper and Pauly‘s (1973) classic are two of the earliest papers referenced in health economics. Most of the papers in this group originate from the US and reflect US institutional concerns. ̳Medical insurance‘ is long-standing health economics territory and the first four items in the group were each cited more than 1000 times. These include Arrow (1963) (cf. Arrow, 2001, reprinted on several other occasions too), which many consider the founding paper of the sub-discipline, Manning et al. (1987) reporting of the RAND Health Insurance Experiment, Pauly‘s (1968; 1991) comment
there are inconsistencies in the reference period for food insecurity questions in the pre-outbreak data.<sup>6</sup> In Ethiopia and Malawi, the reference period in the pre-outbreak data is the last seven days while in the post-outbreak period it is 30 days. In Nigeria, the reference period is consistent (30 days) both pre- and post-outbreak. Finally, in Malawi, only six of the eight questions were included in the pre-outbreak survey.

> 6Online Appendix A includes a the exact wording of each question (including reference periods) in each country both before and after the pandemic began.

9
Respondents in EAP are also more likely to agree with the statement that “EdTech helps all children learn equally” (50 percent) than respondents from outside the region (36 percent). Available data show that EdTech often augments inequality, as it tends to benefit children of higher-income families who have better access to devices and support for using them than children of lower-income families (Yarrow et al., 2022).<sup>8</sup> This is particularly true when used at home rather than at school, as was the case during the pandemic, indicating that at least half respondents are uninformed of the inequality enhancing risks of EdTech.

_Figure 6: Share of officials agreeing with statements on EdTech._ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>MNG PHL LAO IDN VNM EAP Non-EAP<br>average<br>Share of officials agreeing<br><!-- End of picture text --> <!-- Start of picture text --> "EdTech (e.g. Internet, TV, radio, or mobile) was effective in supporting student<br>learning during COVID-related school closures.”<br>"EdTech helps all children learn equally.”<br><!-- End of picture text --> Source: Calculations from CDG–World Bank 2022 microdata.

Interestingly, the views expressed on the extent to which educational technology would help all children (thus not creating further inequalities) correlates with views on gender equity and children with disabilities; respondents who believe that EdTech is neutral are more likely to promote gender equality and favor inclusion toward children with disabilities. We find no correlation between per capita GDP and the views on EdTech,