rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1. INTRODUCTION

The Belt and Road Initiative is the largest infrastructure construction program of the current era, but it has been officially defined only in broad terms. The initiative is described in the Chinese government’s 13<sup>th</sup> Five Year Plan (Ch. 51) as an “all-around opening up in which China is opened to the world through eastward and westward links and across land and sea,” a reference to previous domestic reforms initiated by Deng Xiaoping in 1978. While Deng’s Open Door Policy welcomed the world into China, the Belt and Road are understood to carry the country’s influence out into the world, in part by building a new network of road, rail and port infrastructure to lower trade costs between China and its neighbors. Despite ambition for mutual benefit, concerns have been raised that certain projects are not economically viable, and will leave countries burdened by debt that cannot be repaid by incremental economic growth (Hurley, Morris, and Portelance, 2018; Financial Times, 2018, 2020).

Though a literature has applied quantitative spatial equilibrium models to value transportation investments, which comprise many Belt and Road projects, these models have not yet been widely applied by policy makers in ex-ante project evaluation. Given limited transparency regarding the results of economic feasibility studies for Belt and Road projects, there is an opportunity for quantitative spatial equilibrium models to fill a gap. For this purpose, we develop an original geographic information system (GIS) database that identifies as comprehensively as possible all proposed, ongoing or recently completed road, rail and port projects along six strategic Eurasian corridors laid out in 2
than the disadvantage sector of a market made dual by union or government induced wage rigidities. Nonetheless, segmentation will always be present in all labor markets, even in the absence of unions or minimum wages so long as firms seek to retain workers in whom they have invested. Firms will pay above market clearing "efficiency wages" to lower turnover and in the process, create unemployment or segmentation that may cut across lines of formality. The predictions about the size of the self employed sector and turnover were tested using cross sectional OECD and Latin American data and generally supported.

The size of the informal sector can tell something about the impact of labor legislation on efficiency or distribution, but only if adjusted for demographic and other variables that theory suggests are important. Raw measures of sector size are not a reliable measure of distortions or inequality. The adjusted size of the informal sector suggests that, Venezuela, Peru, Argentina, and perhaps Colombia emerge as having relatively high levels of distortion by global standards, while Brazil, Costa Rica, Panama, Bolivia, Mexico, Guatemala, Honduras, and Paraguay have lower than average distortions. Such crude comparisons of adjusted sector sizes may offer an alternative to meaningless comparisons of earnings across formal and informal sectors. The divergence of these measures from those created from formal labor legislation in _The Long March_ may suggest differences in enforcement, or the ability of the market to work around oppressive regulations.

Comparisons of raw turnover rates across countries tell us little about true labor market flexibility. Theory and preliminary empirical work suggests that many variables affect turnover in significant ways. Once these are considered, Latin American labor markets appear of average flexibility: Paraguay, Honduras, and perhaps Argentina and Brazil appear more rigid than 24
that high-risk firms and firms with fewer tangible assets were more affected by this capital crunch.

While these findings indicate that multinational banks transmitted the crisis from their home to their host countries, Navaretti et al. (2010) find that such banks rather had a stabilizing effect on bank lending during the crisis. They use data from the Bank for International Settlements (BIS) for their aggregate analyses and balance sheet information from Bankscope for their micro-level analyses. On an aggregate level they find that, during the crisis, the share of local assets financed by cross-border funding increases in foreign affiliates. On the micro level, they find that foreign affiliates reduced the ratio of customer loans to customer deposits less during the crisis than domestic banks. They interpret their results as evidence for the wellfunctioning of the internal capital markets of multinational banking groups which supported local assets by cross-border funds.

Berglöf et al. (2009) point out that it is surprising that there were no systemic currency and banking crises observed in emerging Europe despite the high vulnerability of many countries (increased private sector indebtedness, currency mismatches) and the severity with which the crisis hit the region after the Lehman failure. In a country-level analysis using data from the BIS they find that foreign bank ownership mitigated bank lending outflows in the last quarter of 2008 and the first quarter of 2009.<sup>13</sup> Furthermore, they hint at the impact of the political and economic integration with Western Europe without explicitly testing it.

De Haas et al. (2011) use Bankscope data to assess the stability of bank lending in the transition region during the crisis years 2008-2009. Their results show that foreign bank subsidiaries decreased their lending earlier and faster than domestic banks whereas state-owned banks seemed to have been a relatively stable source of credit. They also analyze the impact of home-country government support for the parent banks on their lending activities in emerging Europe because this support might have come with requirements to focus on lending in their home markets. However, they do not find any
closures and high achievement, and larger losses among the highest achieving students in countries with long closures and low achievement.

# **2. Data**

Internationally comparable achievement data in mathematics and science come from the OECD’s Programme for International Student Assessment (PISA). Since 2000, PISA was implemented every three years, with only a longer four-year break between 2018 and 2022 due to the pandemic. Databases include results of 15-year-olds coming from eight cycles of testing, including data from 100 countries and economies. We use data from 72 countries with results available at least from 2022 and one round of assessment before the pandemic. Our sample includes more than 3 million students participating in all rounds.

PISA data include plausible values measuring mathematics achievement comparably across all rounds since 2003. For reading, the results can be compared across all PISA cycles since 2000. For science, achievement scores are comparable since 2006. Every PISA cycle has its main domain, which in practice means that students answer many more questions in this domain and their scores are more reliable than in other domains. The most recent round in 2022 focused on mathematics. Mathematics was also the main domain in 2003 and in 2012, providing us with reliable data on long-term achievement trends in this domain. Thus, in our paper we focus on mathematics results, while we also discuss results for reading and science. Moreover, Goldhaber et al. (2023) demonstrate a much larger impact on math scores for students residing in high poverty areas.

Summary statistics for student achievement and sample size across 2000 and 2022 are presented in Annex Table A1. These data differ from those presented in official PISA reports as we included only countries for which trends could be estimated. While scores in all domains tend to decline, these are not directly comparable since different countries participated in each round. The sample sizes also vary across domains as in different years results for some 5
formal labor regulations.

# 3.3. Descriptive statistics

Table 1 reports summary statistics from the EIS data on the composition and characteristics in the population of registered firms in Turkey. Microenterprises and small firms represent the large majority of formal firms in Turkey. Half of all formal firms are microenterprises with no more than 2 formal employees each; 72 percent of formal firms have no more than 5 employees, and 84 percent have no more than 10 employees. These statistics highlight the importance of having data that cover firms of all sizes with no minimum-employment threshold when assessing the impact of the minimum wage on firms in developing economies.

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||**[14]**Fertilizer requirements by crop|2022|Sri Lanka’s National Fertilizer Secre-<br>tariat (NFS)|
||**[15]**Fertilizer prices|2022|NFS|
|**Fertilizer use**|**[16]**Cultivated area at national level by crop|2022|NFS|
|**and subsidies**|**[17]**Use of organic fertilizer (manure) at national level<br>**[18]**Use of chemical fertilizers at national level<br>**[19]**Fertilizer subsidy rates at crop level|2001-2021<br>2002-2021<br>2013-2022|FAO<br>FAO<br>Department of Agriculture’s Socio Eco-<br>nomics & PlanningCentre|
|**Potential agri-**<br>**cultural yields**|**[20]**Potential attainable yields at crop-district level (all crops rain-<br>fed except rice, which is irrigated; climate conditions of 1981-<br>2010)||FAO-GAEZ|
|**Labor share in**<br>**value added**|**[21]**Employee compensation and value added by agricultural sec-<br>tor|2000|Sri Lanka’s IO table of the Institute of<br>Policy Studies (Amarasinghe and Ban-<br>dara,2005)|
||**[22]** Average wages at district level and total number of mobile<br>workers, estate workers, and farmers at district level|2016, 2019|Sri Lanka’s Household Income and Ex-<br>penditure Survey (HIES), from DCS|
|**Wages,**<br>**land**<br>**ownership,**<br>**
# **2.     Data and Key Variables**

# **2.1.  Data**

The main analysis uses the Labor Force Survey (LFS) data from 2005/2006. Earlier rounds are also used for the trend analysis. The LFS is conducted every two years, collecting a set of information on various dimensions of a country’s civilian labor force, including sociodemographic characteristics, such as age, sex, marital status, level of education, current school enrollment, and migration status, and employment information. Each round of the LFS consists of all urban and rural areas of all provinces defined in the Population Census.<sup>1</sup> The sampling takes two stages. The first stage is the selection of the primary sampling units (PSU),<sup>2</sup> defined as enumeration blocks<sup>3</sup> in the urban areas and mouzas/dehs/villages in the rural areas. The second stage is the selection of secondary sampling unit (SSUs). A specified number of households--i.e. 12 from each urban sample PSU and 16 from each rural sample PSU--are selected using systemic sampling techniques with a random starting point.

Several researchers who have studied the labor market in Pakistan have used the Pakistan Integrated Household Survey (PIHS) or Pakistan Social and Living Standard Measurement (PSLM) Survey. Here I use the LFS because: (1) the LFS data are a representative sample of the labor force; (2) the LFS asks specifically whether the individual (10 years old or above) was actively looking for a job if he/she did not work in the past week, an essential question for defining unemployment; (3) the survey instruments are the same in each round, facilitating trend analysis over a period of years;<sup>4</sup> and (4) the LFS reports the number of hours worked but the PIHS does not.

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**Table 1 The Level of indebtedness of Bulgarian Enterprises**

||_1993_|_1994_|_1995_|_1996_|
|---|---|---|---|---|
|**Long term debt to capital**|||||
|a. mean|0.12|0.12|0.06|0.35|
|b. median|0.00|0.00|0.00|0.00|
|**Short-term debt to capital**|||||
|a. mean|0.48|0.57|0.75|1.53|
|b. median|0.20|0.28|0.37|0.82|

Source: Authors' calculations based on AMADEUS data set (Annex 1).

In the case of Bulgaria, the continuation of SBCs and its relevance for the functioning of the financial sector, imply that in our estimation period, 1993-1996, the SBCs provided perverse incentives with respect to the allocation of external funds (notably bank loans). We will discuss this at length in Section 5 and we will find confirmation for this hypothesis. Here, we merely provide some input for this discussion by showing the financial indebtedness of _loss-making_ firms, see Table 2. We do suspect that these companies faced SBCs with the result that they were able to attract more debt despite their losses. Table 2 shows the financial indebtedness (median and mean) of top 25 and top 100 loss-makers. We calculated means and medians for following ratios: _LOSSK -_ economic losses to long-term assets, _LTDEBTK,_ long-term debt to long-term assets, _STDEBTK,_ short-term debt to long-term assets, _STDEBTAK,_ short-term debt arrears to long-term assets and _LTDEBTAK,_ long-term debt arrears to long-term assets.

The long term debt to long-term assets ratio for top-25 loss makers increased during this period, except in 1995. The ratio of
it examines multiple maternal mental health outcomes for caregivers at the same time; (ii) it differentiates between maternal mental health outcomes for hosts and refugees; (iii) it assesses the severity of various maternal mental health conditions, not just the onset of the condition itself; (iv) it documents differences in child outcomes – both from caregiver reports and from a direct assessment of children; (v) it examines outcomes separately for children between 0-35 months of age and those aged 36-72 months. To the best of our knowledge this is the first study to assess the compounding association of multiple maternal mental health conditions on child development for children 0-72 months in Khyber Pakhtunkhwa, Pakistan.

# <mark>Methods</mark>

## <mark>Research Design and Data Collection Strategy</mark>

This household survey was commissioned by The World Bank and carried out by the Center for Evaluation and Development between December 2023 and February 2024 in Khyber Pakhtunkhwa, Pakistan. Households were selected based on being located in the catchment area of one of 200 public schools across Khyber Pakhtunkhwa (excluding newly merged districts).<sup>2</sup> In most cases, the boundaries of a catchment area represented a maximum of a 30-minute walk from the proximate school. Households living in a given catchment area who had at least one child under the age of 72 months (for either of the 0-35-month or 36-72-month age groups) available to be

> 2 <mark>The sample of 200 public schools was drawn for a different World Bank survey in 2022. These 200 schools are a representative sample of public schools across all districts of Khyber Pakhtunkhwa (excluding newly merged districts). The households in question are thus representative of those in the catchment area of a representative sample of schools.</mark>

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# 3.4 _Baseline controls_

To further raise our confidence against omitted variable bias problem, we control for several potential drivers of female employment. The controls are motivated by the existing literature on the drivers of female employment and discussed in detail below.

We take advantage of the fact that multiple rounds of survey data are available for the same country-industry pair over time. Thus, in all our regressions, we control for dummy variables indicting the country-industry pair to which a firm belongs (Country-Industry fixed effects). Note that the “country” here is the geographical region and independent of ES round. Use of countryindustry fixed effects as controls implies that our regressions control for all time invariant countryindustry specific characteristics. It also implies that all time invariant country characteristics such as culture, social attitudes towards women’s work, legal origin, etc., that may impact female employment are accounted for in the regressions. Similarly, all time invariant characteristics common to firms globally within an industry (that is, industry fixed effects) are captured by the country-industry fixed effects. For instance, studies report a heavier concentration of females relative to males in some industries than in others (Juhn et al. 2014, Amin and Islam 2014, Rendall 2010, Do et al. 2011). Some of the reasons suggested for this in the literature include, for example, the brawn vs. brains content of jobs, the ease with which work can be combined with family responsibility, and culture. Our results are unaffected by such industry-wide features.

Thus, identification of the relationship between female workers and exports comes from differences over time and within a country-industry pair rather than across countries and industries. In this sense, our results are less susceptible to the omitted variable bias problem than is typically the case with pure cross-country regressions.

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, 1101-1164.</mark> Balsmeier, B., & Czarnitzki, D. (2013). How Important is Industry-Specific Managerial Experience for Innovative Firm Performance? In _Academy of Management Proceedings_ (Vol. 2013, No. 1, p. 12060). Briarcliff Manor, NY 10510: Academy of Management.

Barker III, V. L., & Mueller, G. C. (2002). CEO characteristics and firm R&D spending. _Management Science_ , _48_ (6), 782-801.

<mark>Bloom, N., Eifert, B., Mahajan, A., McKenzie, D., & Roberts, J. (2013). Does management matter? Evidence from India.</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>,</mark> _<mark>128</mark>_ <mark>(1), 1-51.</mark> Brynjolfsson, E., Rock, D., & Syverson, C. (2019). _1. Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics_ (pp. 23-60). University of Chicago Press.

Cirera, X., & Maloney, W. F. (2017). _The innovation paradox: Developing-country capabilities and the unrealized promise of technological catch-up_ . The World Bank.

<mark>Cirera, X., & Sabetti, L. (2019). The effects of innovation on employment in developing countries: evidence from enterprise surveys.</mark> _<mark>Industrial and Corporate Change</mark>_ <mark>,</mark> _<mark>28</mark>_ <mark>(1), 161-176.</mark> Cohen, W. M., & Klepper, S. (1996). Firm size and the nature of innovation within industries: the case of process and product R&D.
respect to consumption when looking at welfare over the life cycle, the incidence of VAT is much less regressive. Future analyses can assess the impacts of potential changes to a tax or a benefit, or a combination of several changes, in the system. Simulating such changes would demonstrate further use of this analysis for policy making.

The paper is organized as follows. The next section describes the system of taxes and social spending. Section III presents the data, assumptions, and methodology in estimating the impact, combining household survey, national accounts, and administrative data. Sections IV and V show the results regarding the impact of the net fiscal system and of each fiscal intervention, respectively. The following section concludes with direction for further use of the analysis.

# II. **The structure of taxes and social spending in Serbia**

## **Fiscal revenue**

The public finance system in Serbia consists of:

- _i)_ central government (Budget of the Republic of Serbia, Social security funds – Pension and Disability Insurance Fund, Health Insurance Fund and National Employment Service, and regulatory bodies);

- _ii)_ local self-governments;

- _iii)_ central-level state owned enterprises (e.g. Electric Power Company, Roads of Serbia, etc.) and local-level state owned enterprises (utility companies).

The structure of tax revenues in Serbia is presented in Table 1, and Appendix 3 includes detailed description of the taxes and spending system.

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Table A.24: Comparison between FAT sample and RAIS data (universe)

||Number of<br>employees|Average<br>wage|Share<br>college|Share<br>low-skill|Share high<br>high-skill|
|---|---|---|---|---|---|
|FAT Average (weighted)|28.55|1,311.89|0.05|0.16|0.42|
|RAIS Average (universe)|23.85|1,349.29|0.05|0.17|0.39|
|Estimate (RAIS - FAT)|-4.70|37.40|0.00|0.00|-0.03|
|Standard Error|(3.08)|(29.77)|(0.01)|(0.01)|(0.02)|
|T-Statistic|-1.52|1.26|0.55|0.20|-1.64|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Data from the 2017 _Relação Anual de Informações Sociais_ (RAIS) and the Firm-level Adoption Technology (FAT) survey in Brazil. The estimates from RAIS data are unweighted, and those from FAT surveys are weighted by the sampling weights. Robust standard errors in parenthesis.

112
After the East Asian Financial Crisis in 1997/8, firms which had not implemented mandatory retirement started to do so as a way of slowing wage increases associated with seniority-based wage systems (Cho and Kim, 2005).

In Indonesia, government employees and civil servants face mandatory retirement at 55, and this age is followed by some large private employers as well (Brodjonegoro and Simanjuntak, 2002; Leechor, 1996). From 1995, the “normal retirement age” was considered to be 55 with a maximum age of 60, with these retirement ages applying to larger private sector employers as well as the government.<sup>15</sup> Apart from the “normal retirement age,” however, there are numerous exceptions made through presidential and ministerial decrees, that raise the retirement ages for some occupations. Doctors and other high skilled professional civil servants may retire at 60, more senior levels of civil service are able to retire as late as 65 and university professors face mandatory retirement at age 70. Employees of smaller scale enterprises and the self-employed, who comprise the urban informal sector, do not face mandatory retirement.

The higher employment rates of older urban men and women in Indonesia and Korea (Figure 2) suggest that mandatory retirement from some occupations and types of employers does not mechanically lead to permanent exit from productive employment. Existing research on older workers in both countries suggests that an impending retirement creates incentives for forwardlooking workers to leave employers preemptively, either to start their own businesses or to start second careers working for smaller private sector employers.<sup>16</sup> One significant difference across workers in urban and rural areas of China, Indonesia and Korea lies in the share of the retirement age workforce with access to pensions. In China, evidence from China Health and Retirement Longitudinal Study (CHARLS) pilot suggests that, of urban residents over age 60, 79 and 54 percent of men and women, respectively, have access to pension

> 15Government Regulation No 32 (1979) specifies 55
<mark>Are Poor Individuals Mainly Found in Poor Households? Evidence Using Nutrition Data for Africa</mark> Caitlin Brown, Martin Ravallion and Dominique van de Walle<sup>1</sup>

**Keywords:** Undernutrition, health, poverty, targeting, Africa

**JEL:** I14, I32, I38

> 1 Brown and Ravallion are with the Department of Economics, Georgetown University, and van de Walle is with the Development Research Group, World Bank. The authors are grateful to the World Bank’s Strategic Research Program for funding assistance. Helpful comments were received from Harold Alderman, Arthur Alik-Lagrange, Emanuela Galasso, Adam Wagstaff, and the World Bank’s Africa region.  The authors are grateful to the World Bank’s Strategic Research Program for funding assistance. These are the views of the authors and need not reflect those of their employers.