rafmacalaba/data-use-mentions-extended huggingface dataset:
ing interest payments) instead of paying severance at the time employment terminated.<sup>8</sup> The social security reform of 1993, implemented in 1994, introduced major changes to the health and pension systems, monopolized by the government until then. The reform increased pension and health contributions and thereby non-wage labor (hereafter NWL) costs with the main goal of expanding social security coverage. Aiming to reach universal health coverage, the reform also created two coexisting health regimes: a contributive regime (CR) and a subsidized regime (SR).

In the CR, employers must provide health insurance by law, regardless of occupation,<sup>9</sup> and the cost is shared between employers and employees. In the SR, ‘poor’ individuals who meet certain poverty criteria have access to subsidized health, where ‘poor’ is determined by a poverty index score based on the Census of the Poor (SISBEN).<sup>10</sup> This system is financed with transfers from the contributive regime.

The labor reform of 2002 created a system of social protection, aimed to protect unemployed workers and to promote employment in recessionary periods but had limited impact on job creation, since most of the measures applied to a very limited group of workers and NWL costs were unaffected. The pension reform of 2003 increased the age requirements for retirement, the length of service required for pension, and the pension contribution rate (gradually until 2008), thus increasing employer and employee NWL costs. The health reform of 2007 increased employer health contributions, raising employer NWL costs.

After three decades of rising NWL costs,<sup>11</sup> the more recent labor reform of 2012 reduced employer NWL costs substantially by 8.96 percent through the elimination<sup>12</sup> of para-statal contributions,<sup>13</sup> with the main goal of stimulating employment.

Changes in social security contributions caused sizable changes in employer and employee NWL costs,
.4%|
|**United States**|140|75.0%|16.4%|8.6%|0.0%|**Minerals**|110|74.6%|15.5%|7.3%|2.7%|
|**Germany**|127|79.5%|10.2%|9.5%|0.8%|||||||
|**Japan**|118|67.0%|16.1%|13.6%|3.4%|||||||

_Source:_ Authors‟ calculation based on Lao PDR customs data The same information, for FPDs, can be seen in Table 5, this time disaggregated by sector and destination. The results are quite uniform overall, with no outliers. Concerning destinations, China has the highest percentage of FPDs surviving only one year (~90 percent) and Japan the lowest (67 percent). Japan is also the destination with the highest percentage of long-lived export links (3.4 percent of the FPDs appear in all the four years), whereas China, the US and the EU members have almost no FPDs older than three years. Agriculture has the highest percentage of its FPDs (3.7) lasting 4 years, while the garment and footwear industry has the lowest (0.3 percent), but the sectors seem to be very similar in terms of survival characteristics.

_Exporting from a small landlocked economy_ _13_
Policy Research Working Paper 9968

# **Abstract**

Every year, disasters account for billions of dollars in crop production losses in low- and middle-income countries and particularly threaten the lives and livelihoods of those depending on agriculture. With climate change accelerating, this burden will likely increase in the future and accurate, micro-level measurement of crop losses will be important to understand disasters’ implications for livelihoods, prevent humanitarian crises, and build future resilience. Survey data present a large, rich, highly disaggregated information source that is trialed and tested to the specifications of smallholder agriculture common in low- and middle-income countries. However, to tap into this potential, a thorough understanding of and robust methodology for measuring disaster crop production losses in survey microdata is essential. This paper exploits plot-level panel data for almost 20,000 plots on 8,000 farms in three Sub-Saharan African countries with information on harvest, input use, and different proxies of losses; household and community-level data; as well data from other sources such as crop cutting and survey experiments, to provide new insights into the reliability of survey-based crop loss estimates and their attribution to disasters. The paper concludes with concrete recommendations for methodology and survey design and identifies key avenues for further research.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pwollburg@worldbank.org.

_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
missing or inaccurate, with sometimes the stated plants locations being kilometers away from their actual locations as seen by satellite imagery. As such, we were able to locate 1,346 WWTPs, out of the orginial number 2,743. It is noted that the assets are represented as points in the centroid of the WWTPs, as opposed to the locations of control rooms or elsewhere. In addition, we only focus on complete facilities rather than individual assets within the plants and do not consider exposure differences in treatment stages within a plant nor do we taken into account of the type/quality of outlets which may affect the extent to which WWPTs are impacted post‐disasters. Further, WWTPs not in current operation are not considered.

Using the known WWTPs in the supplied dataset, we further estimate the population dependent on the _w_<sup>th</sup> plant, _Cw_ , as: where V�,� is the daily volume treated in 10,000 m<sup>3</sup> for waste treatment plant _w_ in county _b_ ; _Vb_ is the total waste water treated for county _b_ ; and P� is the population of county _b_ . In some cases, we do not have population data for counties therefore our user sample of WWTPs is reduced to 891. _Vb_ is calculated by equation (2) as shown below, where we aggregate all the waste water treated in any county _b_ : 5
Policy Research Working Paper 8742

# **Abstract**

Students around the world are going to school but many of them are not learning—an emerging gap in human capital formation. To understand this gap, this paper introduces a new global data set measuring learning in 164 countries. The data cover 98 percent of the world’s population from 2000 to 2017. The data set will be publicly available and updated at regular intervals by the World Bank and is designed to serve as a public good to accelerate global policy and research agendas focused on quality education and human capital formation. The paper presents several motivating facts in a first application of the data: (a) although enrollment has increased worldwide, learning progress is more limited; (b) girls outperform boys on learning—a positive gender gap—in contrast to a negative gender gap observed for schooling; (c) human capital when measured by both schooling and learning accounts for between a fifth to half of cross-country income differences—a middle ground in the recent development accounting literature and (d) average estimates mask important underlying heterogeneity by country income status and region. These stylized facts demonstrate the potential of this new global dataset to reveal insights into the relationship between human capital and economic development.

This paper is a product of the Education Global Practice and the Office of the Chief Economist, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/research. The authors may be contacted at hpatrinos@worldbank.org.

_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
**Notably, the considerable expansions in the real minimum wage have been accompanied by a rise in the ratio of minimum wage to median wage, a measure of the relative importance of the minimum wage in the labor market.** This ratio now positions Romania at mid-ranking among OECD nations and is elevated compared to other EU countries. From 2007 to 2017, the minimum-to-median wage ratio surged from 38% to its peak of 60%, placing Romania among the upper ranks of the EU. During these years, Romania transited from the bottom to the top in the ranking of the EU member states by this ratio (minimum-to-median wage). Subsequently, post-2017, this ratio experienced a slight decline, settling at 55%. Similar trends are observed when looking at the minimum-to-mean ratio (figure 2, panel a). Despite slower minimum wage growth post-2017, Romania is still ranked at the top. In 2023, among European OECD countries, only France, Portugal and Slovenia have significantly higher minimum-to-median wages than Romania. In Poland and Luxembourg, these values are similar to those in Romania (figure 2, panel b).

**This ratio is relevant, as it has been frequently used to assess the constrictive nature of minimum wage policies (ILO, 2012).** When the minimum wage is high relative to the median (the so-called Kaitz index (KI)), it is expected to have a more significant bite or stronger “disemployment”<sup>5</sup> effects on the labor market (Medrano-Adán & Salas-Fumás, 2023; Cengiz et al., 2019). Ahlfeldt et al. (2022) found that beyond 50% of the mean wage, increases in the minimum wage result in a progressively greater reduction in employment. Rutkowski (2003) <mark>suggests</mark> this ratio should be at most one-third in countries with elevated unemployment among young and low-skilled workers. In 2022, Romania's unemployment rate for individuals aged 15 to 74 fell within the
they have stopped attending for now either because they are unaware of meetings or because they do not see much point in going. Instead, basin stakeholders seem to have sector-based fora for information sharing (e.g., union of municipalities, chamber of industries, chamber of agriculture, etc.), which may serve operational purposes, but are not conducive to development of an agenda of basin activities.

Given Costa Rica’s size and the centrality of the Tárcoles basin, national entities (ministries and institutes) do most of the monitoring, as evidenced in the State of the Nation project which compiles data from these sources into comprehensive reports. There was not much evidence of institutionalized monitoring of the Tárcoles basin per se. The 1997-98 study paid for by IDB and led by ABT Associates remains the most recent effort at a comprehensive picture of the basin and its conditions.

# **_8.5 Other Factors_**

In each case study, there have been important factors relating to the emergence and success of basin management that were not envisioned in our analytical framework. This is in the nature of research on institutions, which are always shaped by particular contexts and therefore exhibit unanticipated or idiosyncratic features. In this case, at least two additional factors in Costa Rica have shaped the outcomes in the Tárcoles basin so far.

One factor is the relatively large number of separately identifiable river basins in a relatively small (geographically speaking) country. The Tárcoles basin is only one of 34 identified river basins in Costa Rica. In light of the country’s modest size, there is substantial difference of opinion about the appropriate scale on which to try to organize 27
# **1. Introduction**

A dominant political economy concern is that vote buying practices in electoral competition may be associated with weak accountability. The offering of “gifts” or money in exchange for votes at the time of elections has been found to be widespread in poor democracies, despite the existence of secret ballots. Much of the literature has been occupied with the question of how, even in the presence of secret ballots, vote buying might function and be appealing as a political strategy.<sup>2</sup> The equilibrium consequences for public policy performance when governments gain (or lose) office in elections with widespread vote buying, has received relatively little attention.<sup>3</sup> To date, there is no direct empirical evidence on how public spending or service delivery varies with the practice of vote buying.

This paper provides the first such evidence, using uniquely suited micro data. The data reveal a strong negative association between vote buying and the provision of a public service that is known to disproportionately benefit the poor—in primary health. This correlation is surprisingly robust to a host of alternate specifications. Both the richness of the data and the specific institutional context from which they are drawn allow the interpretation of this correlation as the equilibrium policy outcome of clientelist political competition. Where underlying conditions are conducive to the use of clientelist political strategies, such as vote buying, the politicians who win office are likely to use fewer public resources for, and perform worse in delivering broadly pro-poor public services.

It is important to emphasize upfront that the paper’s contribution is the establishment of a robust correlation with a meaningful interpretation, rather than any claims for causality. For example, the results **cannot** be used to argue that engaging in voter education campaigns to reduce vote buying would improve health services.<sup>4</sup> Vote buying in the context examined here is a particularly measurable form of clientelism.<sup>5</sup>
**_<u>________________________________________________________</u>_**

meta-information, documentation and related datasets. DDP is developed and maintained by the Development Data Group (DECDG) and its partners.

The internal database system for the World Bank contains 40 databases from the Bank and other international institutions (IMF, FAO (Food and Agriculture Organization of The UN), UN, ITU, IEA etc.). The time series component of DDP provides access to databases from the World Bank, the Food and Agriculture Organization, the International Monetary Fund, the Organisation for Economic Co-operation and Development, the United Nations, and other international organizations which gather and disseminate international statistics.

The World Bank's central database, GDF & WDI Central combines over 900 key annual indicators taken from many of the other databases and includes the officially published crosscountry comparable data from World Development Indicators (WDI) and Global Development Finance (GDF).

World Bank regional databases contain data directly from country management units in the Bank's six regions. These data are intended for country-focused analysis and may differ from officially published data. Other World Bank databases include those from specialized units across the Bank. Infrastructure data (compiled from various sources) can give an indication of the infrastructure network in many countries, and explain costs of domestic transport. (See <u>Annex H for details)</u> DDP Microdata, developed and maintained by the Development Data Group (DECDG), is a federated repository of survey data and documentation, providing authorized users with access to unit record-level datasets. The catalog is maintained through collaborative efforts by several depositors and holds information on household, investment climate, service delivery and client feedback surveys. A web-based tool allows users to perform online analysis on selected datasets.

# _<u>Best Practices Database</u>_

Over 2,150 award-winning solutions to common social, economic and environmental problems in over 140 developing and developed countries. Searchable by country, scale (global, national, regional
countries beginning from January 2006. Figure 1 shows the locations of these markets across the four countries as well as the number of monthly observations available for each. Appendix Table A.1 presents a breakdown of the number of markets by country as well as markets across regions of Malawi over the sample period. The price data preview is presented in Figure 2, which shows the national median prices and the timing of the export bans in the respective countries. Over the study period, we find that price levels across all four countries have shown signs of seasonal variation within each year as well as fluctuations across the years. Price levels spiked during the 2007–2008 global food price crisis. Following that, from around 2010 onwards, Mozambique and Tanzania had higher price levels, relative to Malawi and Zambia. The figure also shows the periods during which export bans were in place by each country. During the 2008 food price crisis, when countries were forced to respond to sharp food price inflation, both Malawi and neighboring Zambia and Tanzania imposed export bans. Since 2012, export restrictions have been more active only in Malawi and Zambia. Tanzania’s use of export bans was relaxed until recent years. Mozambique has not imposed any ban during the entire study period.

While the comparison of national average prices may be informative, the empirical analysis adopted to study the impacts of export bans on domestic and relative prices uses market-specific prices in Malawi and pairwise comparison of markets within Malawi and markets in the neighboring countries. We identify the closest and the farthest pairs of markets based on distance and travel time. The imputation of distance and travel time is based on road network data from the HERE API.<sup>3</sup> For our analysis, we locate, for each of the 74 markets in MW, the closest markets in country B (where B is MW, TZ, MOZ, or ZM). MW-MW refers to a market pair where both markets are within Malawi, and MW-TZ, for example, indicates
|0.238|0.117|0.202|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

Table 8c: Impacts of PROSPERA on Type of High School: 6<sup>th</sup> Graders in 2008 in Medium, High and Very Highly Marginalized Communities Nearest Neighbor Matching Models

|Variables|General|Technical|Technicalprofessional|Private|
|---|---|---|---|---|
||Propensity score matc|hing with exact|matching on age and state||
|Girls|0.0519***|-0.0317*|-0.0039|-0.0109*|
||(0.0163)|(0.0169)|(0.0045)|(0.005)|
|Observations|6,654|6,654|6,654|6,654|
|Boys|0.0895***|-0.01980*|0.0361|0.0017|
||(0.0214)|(0.0123)|(0.01862)|(0.0049)|
|Observations|4,853|4,853|4,853|4,853|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: Control variables include: age, number of siblings, household size, lives with mother,
contribution of each factor to productivity growth between 2003 and 2008, both individually and as sets of either firm- or country-level variables.

For the EU12, country-level characteristics contribute the most toward explaining productivity growth. Of the variables included, the most influential are DB indices of government business regulation, the availability of credit, and the stock of inward foreign direct investment. The FDI is especially important in manufacturing sectors. Firms with international owners or affiliates grew significantly faster than purely domestic firms.<sup>7</sup> These two effects suggest a role for government policies promoting FDI in improving productivity growth.

Among the EU15, we find that firm-level variables are the most important determinants of productivity—specifically, firm size and ownership. Smaller firms grow more quickly than large firms. Meanwhile, foreign-affiliated firms show much greater productivity gains compared with purely domestic firms: global headquarters grow most quickly, followed by domestic

> 2 Aghion, Acemoglu, and Zilibotti (2006).

> 3 For surveys of the literature, see Wagner (2007); Foster, Haltiwanger, and Krizan (2001); Bartelsman, Haltiwanger, and Scarpetta (2004); Barba Navaretti and Venables (2004).

> 4 The EU12 consists of Bulgaria, Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, the Slovak Republic, and Slovenia. However, Cyprus and Malta are excluded from the analysis due to lack of data.

> 5 The EU15 consists of Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the United Kingdom. However, Luxembourg and Ireland are excluded from the analysis due to lack of data.

> 6 Amadeus is a comprehensive firm-level database containing financial information for over 11 million public and private companies throughout Europe produced from the Bureau van Dijk

> 7This result is common in the literature. See Dunne, Roberts, and Samuelson (1989); Ilmakunnas and Maliranta
aspects of mental health and outlook.<sup>2</sup> Studies on the impact of accessing and using savings products also find positive effects. In particular, the literature has found that providing individuals access to savings instruments increases savings (Aportela, 1999, Ashraf et al., 2010<sup>3</sup> ), female empowerment (Ashraf, Karlan, and Yin, 2010), productive investment (Dupas and Robinson, 2009), and consumption (Dupas and Robinson, 2009 and Ashraf, Karlan, and Yin, 2010).

Using household-level survey data for El Salvador for the period 1995-2001, this study investigates the impact of remittances on financial inclusion. In particular, we focus on whether remittances promote the use of deposit accounts and credit by examining the impact of remittances on the likelihood that households have a deposit account, apply for loans, and receive loans from formal financial institutions.

There are several ways in which remittances could affect financial inclusion. First, remittances might increase the demand for savings instruments. The fixed costs of sending remittances make the flows lumpy, providing households with excess cash for some period of time. This might potentially increase their demands for deposit accounts, since financial institutions offer households a safe place to store this temporary excess cash. Second, remittances might increase household’s likelihood of obtaining a loan. Processing remittances flows provides financial institutions with information on the income of recipient households. This information might make financial institutions better willing and able to extend loans to otherwise opaque borrowers. On the other hand, since remittances might help relax households’ financing constraints, the demand for credit might fall as remittances increase.

To the best of our knowledge, there are only three papers that examine the direct link between remittances and the domestic financial system. Aggarwal, Demirgüç-Kunt, and Martínez Pería (2011) study the impact of remittances on the financial sector using balance of payments data on remittances and aggregate data on bank credit and deposit amounts for 109 developing countries over the