rafmacalaba/data-use-mentions-extended huggingface dataset:
, although one has to be cautious in light of issues related to estimation and measurement. Dam and Scholtens (2008) investigate a cross sectional firm level dataset (EIRIS, AMADEUS, WBES and WDI) covering 44,149 subsidiaries of 540 European Multinational Enterprises (MNEs) in 188 countries and find that increased CSR<sup>8</sup> activity within a firm reduces its likelihood to locate in regulatory pollution heavens. This finding suggests the existence of a broad separating equilibrium (as proposed in Kitzmuller and Shimshack (2012)), where a neutral sector is more likely to follow classical PHH and RTB predictions, while a CSR sector is subject to some mechanism that may dampen classical incentives.

# _International CSR and the Role of Information_

Economists have recently started to analyze strategic, market driven corporate provision of public goods or reduction of public bads and externalities. This behavior has been termed self regulation or CSR<sup>9</sup> . Besley and Ghatak (2007), Bagnoli and Watts

> 8 The authors use a variable called Environmental Responsibility as a proxy for CSR. Environmental Responsibility contains factor scores based on a factor analysis of four indicators from the EIRIS (Ethical Investment Research Service) database ranking all firms on a scale between -1 and 3. The four indicators are Environmental Performance, Impact Improvement, Environmental Reporting and Environmental Management.

> 9 The fact that CSR can be used in a strategic fashion while being perfectly coherent with profit maximization led to the term "strategic CSR" coined by Baron (2001). Potential strategies range from attracting a motivated workforce (e.g. Besley and Ghatak 2005) to selling to socially responsible consumers (e.g. Bagnoli and Watts 2003) to hedging against private (e.g. Baron 2001) or public policy interventions (e.g. Lyon and Maxwell 2004 ).

Page **5** of **27**
He uses 4 repeated cross-sections of the national income-expenditure survey ENIGH (2000, 2004, 2005, 2006) which includes labor outcomes and categories of expenditures. ENIGH data has a total of 76,000 household-heads and 156,247 other members. He does not find a significant result on the probability of being in the formal sector, nor does he find an effect on wages. However, Barros does not control for unobserved characteristics as he uses cross section data. The target intensity may also be a poor measure of the actual intention to enroll households in _SP_ ; the targets are likely to reflect political stances rather than an accurate measure of the availability of _SP_ services in an area.

Bosch et al. (2010) look at the effect of _SP_ over a longer time period and do find a disincentive effect on formal employment. They use aggregate data at the municipality level from the Instituto Mexicano de Seguro Social (IMSS) from 2001 to 2008. They use a difference in difference approach, comparing the differences in formal jobs outcomes in the municipalities with the program and compare to those without the program. They find evidence of a negative impact of the program on formality at late stages of the program (i.e. after 2007) and found that it is larger in small firms compared to micro firms (with already a high level of informality). However, there are limitations to this work. Their data are aggregated at the municipality level, so they cannot control for individual heterogeneity or aggregation biases.

New working papers do use individual level data to look at the impact of _SP_ (Aguilera, 2011; Azuara and Marinescu, 2011; Duval and Smith 2011). However, they only use individual crosssectional data; they do not look at how individuals‘ change their employment status over time nor do they control for unobserved heterogeneity. They also do not take into account that individuals‘ decisions can be affected by the SS status of their spouse, nor do they consider both flows into and out of SS
in supply across food products in the quarter following the outbreak of the pandemic for the 50 most affected countries.<sup>3</sup> Given the substantial uncertainty about the extent of the current health crisis, in a sensitivity analysis we consider an upward scenario, where initial export supply shocks are 10 percent higher compared to the observed declines in China’s food exports, and a downward scenario where we assume that the initial decreases in supply across countries are 10 percent lower compared to the observed declines in Chinese exports of food. These scenarios roughly imply that shocks in the 50 countries most affected by Covid-19 could be half or double the initial shock in China.

Under the baseline (China-like) scenario, Covid-19 is estimated to lower the world’s export supply of food by 12.7 percent, on average in the quarter following the outbreak of the pandemic. Many important staple foods, including rice, wheat and potatoes have drops in export supplies of over 15 percent. Price changes for individual country-products will depend on the concentration of their imports from countries most affected by Covid-19 and on the elasticity of import demand. Using product-level elasticities estimated from a gravity analysis covering 152 countries by Fontagné et al. (2019), we find that lower export supplies would lead world food prices to increase by 4.0 percent on average. In the downward and upward scenarios for the initial supply shocks caused by Covid-19, decreases in overall food export supply would range between 5.7 and 19.7 percent and price increases between 1.8 and 6.3 percent on average. These effects should be considered as initial impacts, as the longer-term effects of Covid-19 will largely depend on the length of the health crisis -something that it is difficult to predict at this stage.

We next quantify the potential impact on export supplies and global food prices of export restrictions. As of the end of April, more than 20 governments had imposed some form of restrictions on food exports (EUI-GTA-WB, 2020). As
# **3. Data**

# **3.1 POEA Micro Data**

The data are from the Philippine Overseas Employment Administration’s (POEA) database of departing OFWs. Created in 1982, POEA is a Philippine government agency within the Department of Labor and Employment. POEA has a multifaceted agenda: it monitors recruitment agencies, monitors worker protection, and conducts a variety of other tasks relating to the oversight of the overseas worker program. Further, as a final step prior to departure, all OFWs are required to receive POEA clearance. Since all OFWs are required to pass through POEA, the agency has a rich dataset composed of all migrant departures from the Philippines. This is the first paper to utilize this rich data resource.

Since all OFWs must pass through POEA, the dataset contains data on departures for all land-based new hires leaving the Philippines between 1992 and 2009 for temporary contract work. New hires are defined as OFWs who are starting a contract with a new employer. These migrants may have previously worked overseas, but the contract that they are presently departing on is new, rather than renewed. For each OFW departure from the Philippines, the database includes name, birthdate, gender, civil status, destination, employer, recruitment agency, contract duration, occupation, date deployed, and salary. Typical contracts are of one or two year durations, with an average duration of 17.7 months over our sample period. Female workers account for 60.6 percent of new hires during this period. The most common occupations are in production (e.g., laborers, plumbers), services (domestic helpers, cooks) and professional occupations (nurses, engineers, entertainers).

To study the flows of migrants in response to fluctuations in GDP, individual migration records are grouped by year and destination country and combined to create a count of the number of migrants to each destination country annually between 1992 and 2009. Table 1 displays the top twenty OFW destinations averaged over the sample
# **BOX 2: Real Estate Tax in Liberia (Okunogbe, 2021)**

**Problem:** Real estate taxes in Liberia, like many low-income countries, is characterized by high non-compliance rates. One significant hurdle in administering the real estate tax is the lack of a property cadastral. In the absence of comprehensive information on property location, ownership and values, tax enforcement cannot be conducted in a systematic way and many properties remain out of the tax net. Only about 5 percent of residential property owners were estimated to be on the tax roll (Olabisi, 2013).

**Technology intervention:** The Liberia Revenue Authority (LRA) initiated a low-cost technology investment to create a new property database. The tax authority recruited and trained young people to use open-source software on tablets to capture the location, ownership, photo and GPS of properties. The “foot soldiers,” as they were called, went door-to-door, conducting a brief survey with the resident of each property to collect ownership information and record property characteristics, including a photograph of the property. This resulted in the creation of a new property database that the tax authority could then use for property tax administration.

**Research design:** The tax authority used the information from this new database to send notices to taxpayers to request payment of real estate taxes. Four types of notices were sent out, with property owners randomly assigned to the different notices (Okunogbe, 2021). The first group received a “Plain Notice” that provided information on the property tax requirement and the procedures to pay. The second group received a “Detection Notice” that used information from the new tax database to personalize the notice: it was addressed to the property owner by name and included a photograph of the property. The third group received a “Penalty Notice” that provided information on the legal consequences of not complying. The fourth group received a “Detection and Penalty Notice” that combined the features of those two notices.
imbalances and exchange rate misalignments), fiscal risks (e.g. rollover and financing risks, market perception of sovereign risk, and sensitivity of the public sector to shocks), corporate sector risks (e.g. leverage and liquidity, and profitability), asset price and market valuation (e.g. real estate and equity market bubbles) and financial market risk attitudes (e.g. asset and market volatility and the global financial stability map). The second block intends to empirically quantify macro tail risks and worst outcomes, attributing probabilities. Finally, systemic implications from models of cross-border bank contagion and distress dependence framework are drawn from financial market data. Large Complex Financial Institutions and different global scenarios simulations are also considered in this block.

Another initiative is the financial stress index (FSI), which aggregates five indicators: the spread of 3- month interbank rate over government bills for the same maturity, negative quarterly equity returns multiplied by minus one, realized volatility on the equity index, the same indicator for nominal exchange rate, and the volatility of the yield in the 3-month government bill. Duca and Peltonen (2011) did a “quartile” standardization to create an indicator between zero and three, in which higher values mean deeper stress. Then, optimal thresholds for policy interventions are calculated for standalone indicators and probabilities of systemic events. Their results showed that asset price misalignments and credit booms are useful leading indicators for systemic events and that models outperform standalone indicators, as they consider domestic and global macrofinancial vulnerabilities.

31
on the coefficients on the interaction of external finance dependence with the relative importance of foreign banks.

Further, we interact a measure of government effectiveness with the indicator of information complexity in Panel C, and with the indicator of contractual complexity in Panel D. These estimates are qualitatively similar to the results in Panels B and C of Table 5a and Table 5b.

# **7 Conclusion**

Using rich data from over 60 countries, this paper has investigated how financial structures affect the dynamics and concentration of the export sector. While confirming prior findings that financial and banking development can boost the expansion of the export sector, the

> 22As noted by Abiad et al. (2008), entry barriers can take the form of outright restrictions on the participation of foreign banks; restrictions on the scope of banks’ activities; restrictions on the geographic area where banks can operate; or excessively restrictive licensing requirements.

21
2|-0.5|
|P1|6.6|-0.1|-0.2|
|P2|3.5|0.0|-0.1|

Source: Authors’ calculations.

As for the BaU scenario, a thorough assessment of the trade scenarios needs to go beyond these aggregate indicators and should rely on more disaggregate poverty and distributional analyses. In search of trade-induced poverty effects, the remaining part of this section considers an array of indicators, from growth incidence curves to poverty statistics estimated on specific sub samples. In particular poverty and distributional impacts are separately measured for the agricultural and non-agricultural groups, the movers and stayers, the rural and urban populations, the regional samples, and the

> 35 Given that the weak Doha scenario does not produce any visible results, in this section we just report results for the full liberalization and the deep Doha scenarios.

42
Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3,687|3,687|7,768|7,768|3,687|3,687|3,687|3,687|3,687|3,687|
|Number of individuals|1,609|1,609|2,182|2,182|1,609|1,609|1,609|1,609|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

35
IV. Data and Metadata We compiled a panel dataset at the Federative Unit (FU) level (including 26 states and 1 federal district) for all FUs and at annual frequency across the overlapping periods from 2010 to 2020 that are available from all data sources. All data are collected from credible national sources including Brazilian Institute of Geography and Statistics (IBGE), Brazilian National Agency of Petroleum, Natural Gas and Biofuels (ANP), Energy Research Office (EPE) of the Brazilian Ministry of Mines and Energy (MME), and Greenhouse Gas Emission and Removal Estimating System (SEEG).

**Fuel prices** for our analysis are final consumer prices at retail stations of automotive fuels. A typical retail price of fuel in Brazil consists of cost to producers (mostly Petrobras) or importers, federal and state taxes ( _i.e._ , CIDE, PIS, COFINS and ICMS), cost of biofuel that is mixed into the fossil fuel, and cost to distributors and retailers. The retail prices are surveyed in ANP’s Fuel Price Survey (LPC) that cover prices of gasoline C, hydrous ethanol, diesel oil B, vehicular natural gas (CNG), as well as Diesel oil B S10 that was introduced at the end of 2012. The survey was conducted weekly around 459 locations across all Brazilian FUs (ANP, 2020). The LPC is the most comprehensive and authoritative survey of fuel prices in the country, and it has been used for developing public policies (ANP, 2020). We obtained data for our analysis from the historical series aggregated at the monthly frequency and FU level, and combined datasets from different time periods, resulting in continuous series from 2001 to 2022. Prices at FU level is weighted by sales of distributors after October 2004. We take a simple average of the monthly values to aggregate it into quarterly and annual series.

We use **GDP** compiled from the income perspective for our analysis. Total income of an administrative region consists of
# **1. Introduction**

<mark>This paper presents the methodology to generate experimental small area estimates (SAE) of poverty in four West African countries: Chad, Guinea, Mali, and Niger, as well as an evaluation exercise using data from another country in the region, Burkina Faso. SAE is a statistical method used to improve survey estimates by integrating survey data with geographically comprehensive auxiliary data (covariates) typically derived from census, administrative, remote sensing, or mobile phone data. Data integration is achieved with the use of statistical models to produce estimates at disaggregated geographic levels that are more accurate and precise than estimates that rely only on direct use of the survey data. More disaggregated estimates are key for a better understanding of how to target interventions for the poorest areas as well as for monitoring the impact of s</mark> uch <mark>interventions.</mark> <mark>Table 1 illustrates the issues with obtaining poverty estimates at disaggregated geographic levels solely from survey data in the countries under study in this paper. The coefficient of variation estimated (cve) is a common measure used to judge the statistical precision of an estimate.</mark><sup>3</sup> <mark>Countries often adopt a maximum threshold for the mean or median cves of the estimates that can be reported, which in practice usually ranges from 0.15 to 0.3. For the countries of focus in this paper, the most recent survey estimates of poverty can be obtained from the 2018 round of the Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM)</mark><sup>4</sup> <mark>which is available at the regional level for each country. The median cve of regional direct estimates produced by the Horvitz-Thompson estimator ranges from</mark> 0.0 <mark>7 to 0.12, which is typically within the acceptable range for publication. However, when we examine direct estimates
Source: Authors’ own estimate based on NSSO and census data.

# **3.4 Social Groups and Female Labor Force Participation**

It is evident that membership in certain social groups affects a woman’s ability to participate in the labor force. Households belonging to marginalized communities are known to have lower levels of income compared to others, thus producing a negative income effect on women’s participation. Moreover, the literature indicates that women in households belonging to upper caste groups are discouraged from engaging in paid work due to the long-standing stigma attached to women’s paid work (Boserup, 2008). To analyze the effect of social groups on the FLFP rate in India, we must first look at the demographic composition of the female population, searching for any effect on the FLFP rate that occurs due to demographic changes in the composition of social groups. The composition of various social groups was largely stable during the period of 1993-94 to 2011-12 (Figure 5). Scheduled castes (SC) and tribes (ST)<sup>5</sup> had jointly comprised approximately 30 percent of the population in rural areas, and about 20 percent in urban areas (Figure 5).

**Figure 5: Composition of Social Groups in Female Population**

<!-- Start of picture text --> 71.4 68.2 67.9 83.8 78.5 78.4<br>16.2 16.4 16.0<br>12.4 15.5 16.1 10.1 6.1 13.67.9 13.08.6<br>1993-94 2004-05 2011-12 1993-94 2004-05 2011-12<br>Rural Urban<br><!-- End of picture text -->

> 5 Through constitutional provisions, the Government of India specifically recognizes a number of historically marginalized social groups, including those from various caste and tribal communities. These groups are referred to as scheduled castes (SC) and scheduled tribes
problem in education technology. The goal is threefold: addressing conceptual and practical challenges in implementing adaptive experiments in policy settings, studying performance of an adaptive research design in a short time horizon where asymptotic performance guarantees may not apply, and, last but not least, informing the actual policy choice problem at hand.

The example we use is an experiment on using phone calls with interactive voice response (IVR) technology to deliver regular short reading exercises directly to parents in Kenya. The calls are intended to encourage parents to read with their children at home, a practice known to improve language acquisition and fluency (Mayer et al., 2019; York et al., 2019; Knauer et al., 2020). The implementer was NewGlobe, an organization that both supports public schools and operates its own community schools in several countries, including the Bridge Kenya primary schools in our sample. Faced with many options for IVR call and exercise designs, NewGlobe was looking to decide which call format (if any) they should roll out to all parents.

We use the IVR experiment to discuss in detail how to approach designing and conducting an adaptive experiment for policy choice – from estimation approaches, to the algorithm used, to research design decisions, for example about sampling and sampling size. In our example, we tested six different IVR call options during the third term of the 2020 school year. The experiment was designed to identify the call format with the highest level of engagement, measured as the number of IVR calls in which the respondent started the reading exercises, and to test whether IVR calls can increase reading fluency. The calls cross-combine two delivery formats for the exercises – parent-led vs. IVR-led reading – with three different ways of matching exercise contents to the child’s reading level, motivated by evidence that targeted instruction can improve outcomes especially in the tails of the distribution (Banerjee et al., 2007; Muralidharan et al., 2019; Doss et al., 2019).

In order to efficiently identify
# **Job Creation and Demand for Skills in Kosovo: What Can We Learn from Job Portal Data?**<sup>**1**</sup>

Calogero Brancatelli, _Goethe University Frankfurt_ Alicia Marguerie _, World Bank_ Stefanie Brodmann _, World Bank_ _JEL classification codes:_ J23, J24, J60 _Keywords:_ Jobs, Skills demand, Online job portal, Kosovo.

> 1 Calogero Brancatelli, Goethe University Frankfurt: brancatelli@econ.uni-frankfurt.de; Alicia Marguerie, Social Protection and Jobs, World Bank: amarguerie@worldbank.org; Stefanie Brodmann, Social Protection and Jobs, World Bank: sbrodmann@worldbank.org. The authors acknowledge financial support from the Multi-Donor Rapid Social Response (RSR) Trust Fund. The authors are grateful for technical support from Arion Rizaj, Shpat Ferizi (KosovaJob/HumanPower) and Fitim Krasniqi, and for comments received from Shpetim Kalludra (Employment Agency of Republic of Kosovo), Kevin Hempel, Mattia Makovec, Harry Munoz, Monica Robayo, Abla Safir and Mauro Testaverde (World Bank), Visar Rexha and Timothy Sparkman (EYE project), as well as participants from the World Bank Youth Employment Conference held in Pristina in May, 2019. This paper is a product of the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data presented in this work.