rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

The business literature has extensively analyzed how delays and unpredictable delivery times influence firms’ supply chain management decisions (Arvis, Carruthers, Smith, and Willoughby, 2011; Kunaka and Carruthers, 2014). With the development of lean retailing and just-in-time manufacturing practices, together with the increased fragmentation of production across the world,<sup>1</sup> timeliness and reliability have become the watchwords of firms involved in supply-chain trade. Import and export activities have grown increasingly intertwined, with foreign inputs typically imported and incorporated in the production of final goods or transformed and exported to other countries where they may further enter as intermediate inputs in the value-added exported to third countries (Koopman, Powers, Wang, and Wei, 2010; Koopman, Wang, and Wei, 2014). Against this background, unexpected factors such as late shipment arrivals, including truck breakdown-related transport delays, or uncertainty over the time required to clear the consignment after it has reached destination, owing for instance to excessive formalities and physical inspections by border authorities, can lead to postponed delivery of key inputs, thereby disrupting the supply chain. Such uncertainty carries reputational costs for the importing firm, undermining its ability to meet buyer quality requirements and delivery deadlines, and exposing it to the risk of losing sales contracts, market shares and valued customer relationships. Unexpected delays in delivery and their potential snowball effect across the supply chain (Harrigan and Venables, 2006) prompt importers to adopt costly hedging strategies, such as switching to more reliable but expensive transportation modes (Hummels, 2001) or maintaining safety stocks. Evidence suggests that inventory holding to guard against uncertainty generates costs that can vary from 15 percent of the cost of goods per year to as much as 50 percent; reaching on average 27 percent of the goods value in the case of the US (Clark, Kozlova, and Schaur, 2016).

Several studies have formally quantified the effect of timeliness on trade volumes. Based on disaggregated import data for the US,
equipment to treat cases such as COVID-19 may become life-threatening for some of the most critical patients (Forbes<sup>8</sup> ).

Additionally, rural accessibility may become critical to ensure social and economic recovery of rural areas postcrisis. Rural accessibility gains have a strong correlation with growth in agricultural production (Limi, You, and Wood-Sichra 2017), school attendance rate ( <mark>Vasconcellos 1997),</mark> and child mortality rates (Gage and Calixte 2006). Strong evidence has motivated governments and financing institutions to prioritize interventions to enhance rural accessibility. In 2006, the World Bank Group developed the Rural Accessibility Index (Roberts, Shyam, and Rastogi 2006) as a metric to support the prioritization of rural roads investment with the aim to close the accessibility gap that most rural areas suffer from.

The methods and calculations used to determine the Rural Accessibility Index have evolved quickly since its inception, and in recent years, driven also with advances in analytics, availability of geo-reference data, and computing power, several new methods have been developed (Transport & ICT 2016; Mikou et al. 2019). To keep these methods simple and straightforward, the calculations of this index and the prioritization of interventions have been focusing on the origin of population, for example, where rural people live, but they do not consider the destination of that access and the facilities to which they are getting access. A few recent studies have demonstrated that calculating accessibility to specific facilities (for example, hospital, water sources, schools, or employment opportunities) using geo-spatial analysis is feasible to support the prioritization of rural investments (Andres et al. 2018; Krambeck et al. 2019; Quiros, Kerzhner, and Avner 2019). Currently, the World Bank is updating some of the methodologies for the Rural Accessibility Index and developing a toolbox to allow road agencies to calculate this index without the need of a geo-referenced Road Asset Management System.

There is evidence that disruptions of road infrastructure due to climate impacts have
vector, denoted as _p_<sup>_s,i,j,k_</sup> ( _θ_ ). Then it becomes possible to calculate the log-likelihood contribution of an agent from origin _i_ , living in _j_ , and moving to _k_ as log _p_<sup>_s,i,j,k_</sup> ( _θ_ ) for any given _θ_ .

# **3.1 Likelihood Function:**

In the American Community Survey (ACS) data, as discussed in the data section above, we have detailed mobility and birthplace information for people who migrated to the United States. Transit migrants are defined as the people born in _i_ and living in _j_ before moving to the US. We denote their number as _M_<sup>_s,i,j,U_</sup> .<sup>11</sup> Next, using the stocks of migrants born in _i_ and living _j_ in 2000 from the global bilateral migration database and DIOC (see Artuc et al, 2015, and Arslan et al, 2014, for details) and _M_<sup>_s,i,j,U_</sup> matrix, we calculate the approximate number of people who are staying in their current country or moving to destinations other than the US. We denote this matrix as _M_<sup>_s,i,j,S_</sup> . In other words, we divide the migrants born in _i_ living in _j_ ( _i_ = _j_ ) into two groups: those who move to the US, given by _M_<sup>_s,i,j,U_</sup> , and those who moved elsewhere or stayed in _j_ , given by _M_<sup>_s,i,j,S_</sup> .

Using these data matrices, vectors and implied migration probabilities, the log-likelihood con-

> 10Fortunately, the equations can be solved by iteration since _F_ is a contraction mapping.

> 11Note that the United States is
report that it was useful for their daily work (7 _._ 8 _pp_ for 5<sup>_th_</sup> grades and 12 _pp_ for 9<sup>_th_</sup> grades).

Next, we analyse textbook quality and distribution. Using the SAEB questionnaire of principals, the estimates indicate that schools in _Ceará_ are 10 _pp_ to 25 _pp_ less likely to report a lack of textbooks than similar principals on the other side of the border. This reduction only occurs after TA is introduced. At the same time, 5<sup>_th_</sup> grade teachers in _Ceará_ report that textbooks are at least “ ” “ ” _good_ or _great_ around 9 _._ 5 _pp_ more frequently than their counterparts. Considering concerns raised in previous literature regarding the impacts of textbooks on learning (Glewwe et al. 2009), note that the state of _Ceará_ structured these textbooks to support over-age students. Thus, the distribution of good quality textbooks to municipal schools is argued to be a mechanism explaining our findings.

The last group of mechanisms analyzed is related to pedagogical practices. During “RBF+TA” periods, 5<sup>_th_</sup> grade students in _Ceará_ became 2 _pp_ more likely to report that their teachers checked their homework (in both mathematics and Portuguese) than students studying in schools located in the circumventing municipalities outside of _Ceará_ . For 9<sup>_th_</sup> grades no significant result was found for this particular outcomes. Importantly, teachers in _Ceará_ were around 8 _pp_ more likely to cover at least 80% of the school curriculum for both grades, and 11 _._ 6 _pp_ for 9<sup>_th_</sup> grades teachers, after the RBF rule was reformulated in 2013.

We also show evidence that the introduction of the RBF increased public spending on education per pupil by 1 _._ 2% relative to the baseline mean in the municipalities of _Ceará_ when compared to municipalities across the border.
on the impact of potential rail investments and of reductions in the time taken to cross borders in an area of Central Asia that spans the seven countries indicated in Figure 1. Each of these countries except Turkmenistan contains multiple cells, and these are listed in appendix 1. The model is calibrated to data on employment, population, land area, national accounts and trade. It also uses a network of current transport times and their projected changes. We discuss each of these sources of data in turn.

**_Employment:_** Employment data were gathered on a country by country level. For China, the data for the three Western cells, Gansu, Xinjiang, and Qinghai, come from the National Bureau of Statistics in China, http://www.stats.gov.cn.  For Kazakhstan, the data come from the Agency of Statistics of the Republic of Kazakhstan http://stat.gov.kz, reported at the sector and province level. In Kyrgyzstan, employment data at the region and sector level are from the National Statistical Committee of the Kyrgyz Republic http://www.stat.kg/en/. In Uzbekistan, the sector and region employment data are from the State Committee of the Republic of Uzbekistan on Statistics, https://www.stat.uz/en/. For all these countries, we select data from 2016. In Tajikistan, regional data are calculated from the Labor Force Survey 2016, Statistics Agency of Tajikistan. In Pakistan, cell and sector data are from the Labor Force Survey 2014‐2015 available from the Pakistan Bureau of Statistics http://www.pbs.gov.pk. In Turkmenistan, data are only available at the national level, broken into sectoral employment http://www.stat.gov.tm/. We therefore only have one location within the country in our analysis.

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opportunities and obstacles will vary significantly from country to country, the new policy framework and CIRI will help country-level policymakers to identify the full range of actions needed to make clean energy investment more attractive to both domestic and foreign investors.

The index will also help these policymakers understand the institutional, regulatory, and legislative systems that need strengthening; how deficiencies in these systems may impede the flow of private investment in climate-friendly technologies; and the potential impact of various actions. The index will also aid countries in assessing their progress toward a low-energy/carbon growth path, and inform assistance and cooperative efforts.

> 3 While a number of other sectors such as transportation, agriculture, and forestry are also crucial from the perspective of attracting climate-friendly investments, the study focuses on the RE and EE sectors, because these are the sectors where private sector investment activity is particularly strong. Further investments in these sectors could also improve the investment climate for, e.g., clean transportation technologies such as electric vehicles.

> 4 There are other similar initiatives underway that attempt to capture/benchmark countries progress towards climate mitigation or clean energy policies and measures. Notable among them are IADB/Bloomberg‘s ClimateScope, American Council for an Energy-Efficient Economy (ACEE) report ranking countries on energy efficiency and World Energy Congress (WEC) Energy Policy Index, and EBRD‘s CLIM Index.

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hours) Basic Life Skills Training (BLST) at the start of the program, which aims to improve the soft skills of project participants.

The project operates on a continuous basis, with new batches/intakes of youth (of about on average 500 per intake) participating in a project every few months, though the numbers recruited per intake are now increasing.<sup>6</sup> The new batches/intakes are usually taken from districts that have not previously participated in the project. In total, the project aims to reach approximately 10,500 disadvantaged urban youth between the ages of 16 and 35 who have limited social and economic opportunities in its first five years of operation. So far, as of December 2015, about 8,500 youth have participated in the project. The project was recently extended an additional two and a half years, and is now expected to reach 15,500 youth over a period of 7 years.

The project’s rigorous data collection system is the first of its kind in PNG. All youth who expressed interest in participation were first screened through the Eligibility Screening Survey (ESS) instrument, which, in addition to verifying the basic eligibility criteria (such as age and residency requirement), also collects information on the key socio‐economic characteristics of youth. In selected intakes, youth who were deemed eligible, and were selected for participation in the project, were then given the Eligibility Baseline Survey (EBS), which collected a richer set of socio‐economic variables compared to ESS, including data from a survey module on crime and social behavior.

As part of the impact evaluation effort, a control group was identified in districts around Port Moresby that did not participate in the project.<sup>7</sup> The baseline survey instrument was administered to both the “treatment” group (youth selected for the 4<sup>th</sup> and 5<sup>th</sup> intake of the Project),
following Bloom et al. (2019a). Survey data on managerial practices, mark-ups, technology adoption and innovation are merged with other firm performance data such as profits, sales per employee and TFP from the FINA database.

One objective of this survey is to provide robust evidence on the link between management and firm performance in Croatia and in Eastern Croatia (or Slavonia).<sup>3</sup> The overall analysis may not allow to causally relate management capabilities to performance of firms but provides support for specific interventions aimed at improving firm productivity and for piloting policies and programs. For example, our analysis helps benchmark management capabilities in Eastern Croatia and in Croatia overall compared to other countries, the types of management practices that seem more relevant for firms, and the characteristics of enterprises whose management capabilities help explain further differences in performance.

We present some stylized facts for firms in Eastern Croatia and compare them with other regions in Croatia. We find that, _first,_ when benchmarking management scores in Slavonia and in Croatia against the United States, it is noteworthy that the average management score, just like TFP, is higher in the United States than it is in Croatia, and even higher than in Slavonia (Tables 1a and 1b). The average manufacturing firm in Eastern Croatia scores 0.532 in adopting advanced management practices, a value very similar to that in the rest of Croatia (0.542) but behind the one in the United States (0.615, Bloom et al., 2019a). Comparing Croatia’s management practices with those in Russia (Grover and Torre, 2019) and Mexico (Bloom et al., 2019b), other countries for which comparable data are available, we find that an average manufacturing firm in Croatia is better managed than its counterparts in Russia (average score, 0.43) and Mexico (average score, 0.39).<sup>4,5</sup> _Second,_ weaknesses in management practices particularly relating
Kinks in the US Tax Schedule.” _American Economic Journal: Economic Policy_ , 12(3): 402–32.

- **OECD.** 2023. “Tax Wedge.” `https://data.oecd.org/tax/tax-wedge.htm` .

- **OECD.** 2024. “Parental Leave Systems.” `https://www.oecd.org/content/dam/oecd/en/ data/datasets/family-database/pf2_1_parental_leave_systems.pdf` .

- **Olivetti, Claudia, and Barbara Petrongolo.** 2017. “The Economic Consequences of Family Policies: Lessons from a Century of Legislation in High-Income Countries.” _Journal of Economic Perspectives_ , 31(1): 205–230.

- **Paulus, Alari.** 2015. “Tax Evasion and Measurement Error: An Econometric Analysis of Survey Data Linked with Tax Records.” University of Essex, Institute for Social and Economic Research Working Paper 2015-10.

- **Rocha, Rudi, Gabriel Ulyssea, and La ́ısa Rachter.** 2018. “Do lower taxes reduce informality? Evidence from Brazil.” _Journal of Development Economics_ , 134: 28–49.

- **Rossin-Slater, Maya.** 2018. “Maternity and Family Leave Policy.” In _The Oxford Handbook of Women and the Economy_ . , ed. Susan L. Averett, Laura M. Argys and Saul D. Hoffman. Oxford University Press.

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measuring the quality of management practices across 18 dimensions related to operations, planning, monitoring, and human resources, covering thousands of firms in many countries.

Similar to FAT, data on management practices is collected via firm surveys. Experts rank practices based on their quality, and an establishment-level score is constructed to study the drivers of management practices and their association with productivity. We perform a similar analysis for technology sophistication. Beyond the similarities in measurement methods, Bloom, Sadun and Reenen (2012) have hypothesized that technology sophistication and management practices are complementary. We explore the complementarity of technology and management in the context of productivity regression across establishments, finding supporting evidence.

The rest of this paper is organized as follows. Section 2 introduces the FAT survey, and describes various validation exercises of the sophistication rankings, and the data collected. Section 3 presents the technology sophistication measures and illustrates key insights with examples from specific establishments, and sectors in FAT. Section 4 explores the use of technology at the business function level. Section 5 studies technology sophistication across establishments. Section 6 investigates the relationship between technology sophistication and productivity across establishments. Section 7 concludes.

# **2 The Survey**

The FAT survey (henceforth, “the survey") collects detailed information for nationally representative samples of establishments in agriculture, manufacturing, and services about the technologies each establishment uses to perform key business functions necessary to operate in its respective sector. In the following sub-sections, we describe the survey design and implementation, relegating further details to section A in the Appendix.

## **2.1 Structure**

The survey is composed of five modules. Module A collects information on the general characteristics of the establishment.<sup>3</sup> Modules B and C cover the technologies used. Module D focuses on barriers to, and drivers of, technology adoption, while Module E gathers information about the establishment’s financial statements and employment.

> 3The survey is designed, implemented, and weighted at the establishment
/sup> Historical malaria estimate from 1937-41 is used in this paper to identify the effects of land market restrictions in 2002 (survey year for our data), (ii) the timing of the malaria eradication program was determined by technological breakthrough (DDT), and thus can be treated as plausibly exogenous,<sup>15</sup> and (iii) most of the current population in a subdistrict devastated by high historical malaria were never exposed to historical malaria there, as they were resettled

> 12We use the data on malaria endemicity at the district level for 1937-41 compiled by Newman (1965). Emran and Shilpi (forthcoming, 2015) exploit historical malaria incidence to analyze the effects of land restrictions on male and female wages, and women’s labor force participation.

> 13Over time, restrictions on mortgage have been relaxed when dealing with public banks.

> 14Malaria endemicity is measured by enlarged spleen rates. Reported malaria cases in Sri Lanka were reduced from about 3 million per year during pre-eradication era to only 29 in 1964 (Harrison, 1978). The number of malaria death cases were 30 in 2002 among a population of 21 million. The reported malaria death were 4 in 2003, and 0 in 2005.

> 15Although DDT was first synthesized in 1874, its insecticidal properties were discovered in 1939 by Swiss scientist Paul H Muller. It was widely used during second World War to control malaria and typhus, and after the war DDT was made available as an agricultural pesticide and for malaria eradication programs.

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Policy Research Working Paper 9930

# **Abstract**

This paper examines international temporary migration as an intermediary step among aspiring entrepreneurs to accumulate the needed capital when they face credit constraints at home. The analysis is based on a representative dataset of lifetime employment histories of return migrants from Bangladesh. After establishing the credit constraints that potential entrepreneurs face, the paper shows that non-agricultural self-employment rates are significantly higher among returning migrants—over half versus around 20% of non-migrants. Most migrants transition into self-employment by using their savings from abroad as the main source of financing. The paper then offers, for the first time, a detailed account of the financial costs and benefits of international migration. The findings suggest that temporary migration can contribute to structural transformation of lower-income countries by enabling credit-constrained workers to enter into non-agricultural entrepreneurship.

This paper is a product of the Social Protection and Jobs Global Practice the Development Research 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 lbossavie@worldbank.org, josephsimon.goerlach@unibocconi.it, cozden@worldbank.org, and hwang21@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 are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team