rafmacalaba/data-use-mentions-extended huggingface dataset:
important exception, McCaig and Pavcnik (2018), shows significant reallocation of labor following a positive export shock in Vietnam. This study suggests that it is possible that export shocks may be disseminated through national labor markets in ways consistent with neoclassical theory.<sup>2</sup> This study compares the empirical approaches from recent studies of local labor market effects of imports with estimates of general-equilibrium predictions of neoclassical models using repeated cross-section worker-level data from Bangladesh. Bangladesh provides an excellent opportunity to evaluate the national labor-market dissemination of exports shocks for four reasons. First, since the 2013 Rana Plaza collapse, Bangladesh has been at the center of the debate about developing country exports.<sup>3</sup> Second, between 1980 and 2000 Bangladesh experienced a fundamental reorientation of its economic paradigm and production toward an open economy. When the MultiFibre Arrangement (MFA) ended December 31, 2004, demand for Bangladesh’s apparel exports increased dramatically. Although these changes in policy were arguably exogenous to local labor market outcomes, we use an instrumental variable technique to isolate the increase in exports due to changes in world demand to address concerns about endogeneity of the export shock.

Third, Bangladesh’s export growth was highly concentrated within the “ready-made” garments (RMG) sector. South Asia’s export portfolio is less diversified than other developing-country

> 1 Artuc, Chaudhuri, and McLaren (2010) developed a model to estimate worker-level adjustment costs. The seminal work of Artuc et al. (2010) brought labor-market adjustment costs to the center of a new wave of trade models because they show that adjustment costs critically affect welfare implications and that, in some cases, high adjustment costs may overwhelm the positive benefits of trade liberalization.

> 2 Goutam et al. (2017) carry out a similar exercise for Bangladesh.

> 3 See Elliott and Freeman (2003) describing early concern about sweatshops in developing countries.

2
approach.<sup>3</sup> Their basic idea is that allocative efficiency is maximized when firms within a narrowly defined industry can access resources until their marginal revenue products are equalized. This occurs as more productive firms grow until the decrease in their prices matches their higher total factor productivity. Under the HK framework, large dispersions in marginal revenue products among firms operating within a narrowly defined industry imply misallocation of resources in that industry. Specifically, HK consider an economy where each firm produces according to a standard Cobb-Douglas function (with sector-specific factor shares) and outputs can be aggregated through a CES function for each industry. The firmspecific output and capital/labor may be subject to distortions (or wedges/taxes, in the language of the authors). Bringing their framework to the data allows to compute two measures of aggregate productivity: the “actual” one where distortions are present and the “potential” one where these wedges are eliminated. Comparing these two measures allows to quantify the potential productivity gains by increasing the efficient allocation of resources. This methodology does not

> 2 There is a large literature on each of these potential sources of misallocation; the suggested references rely on data from Southeast Asia, the region at the center of this paper. Interestingly, although not surprisingly, none of the individual channels by itself is able to explain the magnitude of misallocation estimated using the indirect approach discussed later. A given economy may suffer from multiple sources of misallocation at once.

> 3 HK measures misallocation without precisely identifying the sources of misallocation. They broadly classified the sources of distortions as capital and output market related distortions. The essence of their findings is that resource misallocation affects productive firms’ access to sufficient resources (in terms of capital and labor) needed for expansion, and this results in lower aggregate productivity. Therefore, reallocation of resources through the elimination of distortions in the markets is productivity enhancing as this allows productive firms to grow larger, and the less productive ones to either contract their operations or exit from
when using the hourly data, in contrast to what we find when using only one year for comparison. Additionally, the local calibration coefficient at the daily level is much closer aligned to the coefficient using the regulatory data. This is in line with our findings that the calibration at the daily level provides a better fit (Table A2). This highlights two points. First, it demonstrates the benefit of having higher resolution hourly data, especially in cases where there is a shorter time-series available. It also shows that in cases where there is a longer time-series, using a lower temporal aggregation (daily or higher) for calibrating the low-cost

> 23For the satellite and regulatory data we analyze 2017-2020 due to data availability and for the low-cost data we analyze 2020, 2022 and 2023, removing 2021 since some policies were still in place at that time.

26
financial difficulties (JJii = α + βQQii + ρωωii + ∀XXii ii + εεii ), the use of new or expanded electronic platforms ( ), and the share of work from home ( ); is a vector of exogenous ii ωω FF ii AA EEIIssh XX 15 We elect a standard econometric approach to arrive at the best linear unbiased estimates of coefficients on explanatory variables. We estimate multiple versions of the same equation and use a T-test of the significance of an included regressor is a test of whether it should be included in the model. We found the coefficient estimates to be generally stable. In general, we prefer a parsimonious model. This increases potential applicability to other contexts and improves efficiency of the estimations. For some tables, we report insignificant regressors of interest, nonetheless. Generalized residuals that are not significant show that the correction is not a significant determinant of the outcome variable and should be excluded. Cross-validation is another method for model selection and provides the best linear unbiased predictor, but our main focus in this phase is to estimate and test the significance of various contributors to job loss. In practice, cross-validation would be computationally complex and impractical when there are multiple stages in the general causal model.

16 In practice, we find that unobservable determinants of the propensity to receive policy support were not significant in later stages of the estimation.

17 We cannot model firm entry and exit given data limitations. The sampling frames were not refreshed with each round of the CFUWBES. Moreover, we cannot model firm closures as of round 1 within the same model structure as that used for firms in operation, because variables are not captured for those that have shut down. For example, some of those firms may have received wage subsidies or had supply chains disrupted after round 1, but this is not observed in round 2. Because we attempt to control for selection bias, whether due to non response or firm exit, we still in principle have unbiased impact estimates. Moreover, it does not appear that the inability
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data relevant for many other developing (transition and non-transition) countries where we may expect serious underreporting.

We will argue that the MIMIC approach provides the more accurate estimate of the extent of underreporting because it incorporates firm-level incentives to comply with taxes and a production function technology linking true sales with observable firm-level production characteristics, it controls for measurement errors, and it allows for underreporting in both official tax and survey data. According to the MIMIC approach, the average percentage of underreporting to the tax office is 38.6% at the firm-level and 37.5% at the aggregate for the population of firms from which the sample has been drawn. The indirect approach performs poorly and underestimates underreporting because it is sensitive to measurement errors and underreporting in the survey data. The direct approach gives an estimate of the firm-level average percentage of underreporting which is somewhat lower than the MIMIC approach, confirming the conjecture of Schneider and Enste (2000, p.92) that the direct approach provides a lower bound for true underreporting. However, the direct approach gives a too high estimate for aggregate underreporting because of measurement error and appears less useful as an indicator of underreporting by individual firms.

The remainder of the paper is organized as follows. In section 2 we discuss the extent of underreporting by firms around the world based on the commonly used self-reported measures of underreporting (direct approach). In section 3 we provide a formal derivation of a MIMIC model to estimate the true sales of a firm. In section 4 we discuss the empirical 6
<sup>2</sup> around the world. We spatially overlay high-resolution flood inundation maps for ten flood return periods: 5-year, 10-year, 20-year, 25-year, 50-year, 75-year, 100-year, 250-year, 500-year, and 1000-year (based on Fathom global flood models at 3 arc-seconds spatial resolution, equivalent to about 90 × 90 meters at the equator<sup>3</sup> ) with worldwide road networks extracted from Open Street Map (OSM) to better understand local hazard exposure. For each return period, we simulate travel trajectories between potential origins and destinations (generated with a probability-based spatial sampling approach) within each settlement cluster and record trip details such as travel time, distance and trajectory. Our study fills a gap in the current literature by providing the first global evaluation of both direct (impacts due to the physical contact with floodwater) and indirect (impacts not due to physical contact or that occur outside the inundated area in space or time such as travel disruptions and cascading failures in interconnected or interdependent infrastructures) flood hazard impact on road transportation networks.

# **Literature Review**

Floods are the predominant cause of weather-related disruptions to the transport sector<sup>44</sup> . Specifically, floods reduce transportation network capacity, either directly through physical destruction rendering roads unusable or through flood water accumulation on the road surface rendering the road impassable<sup>2</sup> . These local network disruptions often cause increases in travel demand on distant road segments leading to severe traffic congestion that could later propagate to regional scales<sup>25,45,46</sup> . In the following sections, we present a review of existing literature on understanding direct flood hazard exposure and vulnerability and resilience of transportation networks to floods.

## **Understanding exposure**

> 2 Here we used the World Bank-approved administrative boundaries (Admin 0) as reference. In sum, there are 251 countries and regions worldwide and the settlement clusters identified in our study spatially intersect with
infrastructure, faculty, link with productive sector (LPS), costs and funding, and other practices related to student admission and institutional governance. By complementing WBSCPS data with administrative program-level information from official sources and program websites, we obtain a novel, multi-country dataset with program-level information on student academic and labor market outcomes as well as quality determinants and characteristics of the program, institution, and student body.

In the regional approach, we exploit this dataset to find the association between program-level outcomes (dropout rate, time to degree, formal employment, and wages) and quality determinants for the five survey countries. For the country-level approach, we complement program-level information with individual-level administrative datasets from Brazil and Ecuador, which combine information from higher education censuses, national high school learning assessments, and social security records for each country. We exploit these sources to estimate the contribution of program quality determinants to student-level outcomes (formal employment and wages for both countries, plus graduation rate for Brazil) while accounting for student and peer background characteristics and former labor market experience as well as program and higher education institution (HEI) characteristics. To our knowledge, this is the first paper to measure the associations between a large set of quality determinants and academic and labor market outcomes by exploiting programand individual-level data for multiple countries.

This wealth of information allows us to characterize the SCP sector well beyond what was previously possible and to examine the association between student outcomes and program quality determinants. The sheer amount of information, however, poses the challenges of selecting the right set of explanatory variables (to avoid both omitted variable bias and model overfit) and overcoming the confirmation bias from selecting only the variables that confirm researchers’ 4
# **References**

- Abay, Kibrom A., Guush Berhane, John Hoddinott, and Kibrom Tafere. 2021a. COVID-19 and Food Security in Ethiopia: Do Social Protection Programs Protect? _Economic Development and Cultural Change._ <u>https://doi.org/10.1086/715831.</u>

- <mark>Abay, Kibrom A., Mulubrhan Amare, Luca Tiberti, and Kwaw S. Andam. 2021b. COVID-19-induced disruptions of school feeding services exacerbate food insecurity in Nigeria.</mark> _<mark>The Journal of Nutrition</mark>_ <mark>151, no. 8 (2021): 2245-2254.</mark>

- <mark>Aiken,</mark> Emily, Suzanne <mark>Bellue,</mark> Dean Karlan, Chris Udry <mark>, and</mark> Joshua E. Blumenstock <mark>. 2022. Machine learning and phone data can improve targeting of humanitarian aid.</mark> _<mark>Nature</mark>_ <mark>603: 864–870.</mark>

- Almenfi, Mohamed, Breton, Melvin; Dale, Pamela; Gentilini, Ugo; Pick, Alexendar; Richardson, Dominic. 2020. Where is the Money Coming From? Ten Stylized Facts on Financing Social Protection Responses to COVID-19. Social Protection and Jobs Policy and Technical Note; No. 23. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/34802

- Alloush, Mo,  Jeffrey R. Bloem, Jonathan G. Malacarne, 2022. Social protection amid a crisis: new evidence from South Africa’s older person’s grant. Working Paper.

- Amare, Mulubrhan, Kibrom A. Abay, Luca Tiberti, and Jordan Chamberlin. 2021. Impacts of COVID-19 on food security: Panel data evidence from Nigeria. _Food Policy_ 101(May): 102099
Table 3, Panel A looks at differences across time of enterprises by sector for the control and isolated groups separately. The differences are calculated using 1996 performance as a benchmark. A negative Z-statistics on employment, for example, would mean that the firms have reduced employment between 1992 and 1996. A positive sign on profitability would indicate that firms improved their performance during 1992-96. Finally, a negative sign on subsidies would indicate hardened budget constraints. In all cases, the coefficients are statistically significant if they are above 1.96 in absolute magnitude. Since agriculture constitutes about half the sample for both sets of firms, it is possible that it drives the overall result. I hence also run the Wilcoxon Test on all sectors excluding agriculture. The results are reported in the next to last row of Table 3, Panel A.

Across sectors, in both the control and the isolation groups, the Wilcoxon test statistics indicate that employment was reduced significantly between 1992 and 1996. The largest reductions occurred in agriculture - Z-stat of -3.485 and -7.113 for the isolated and control groups respectively. Although the isolation program resulted in labor shedding, I show that more drastic labor shedding took place in firms outside the program, despite their lack of access to redundancy payments. Profitability went down for both groups: Z- stats of -4.072 and -0.301 respectively. This decline was driven primarily by the worsening of the agricultural sector. Once agricultural farms were excluded, profitability in both groups actually increased (Z-statistics of 2.148 and 2.718 for isolated and control groups).

Significant positive improvements in profitability were recorded in three manufacturing sectors (Non-Metallic Products; Chemicals; Metallurgy) in both groups. This is indicative of strong industry effects. Subsidies were cut in all sectors with the exception of agriculture. Again, control group firms were cut off from subsidies more than their comparators under isolation - the respective Z-stats are -5.127 and -8.448.

Panel A compares firm
s socio-economic or family background. In 2019, it included data on about 176,000 schools, 2.3 million teachers and 50 million students, with 20,272 (0.05% of all students) Venezuelan students in regular traditional school, all over Brazil. In 2020, Brazilian students in regular school increased to 37,738.

The RAIS dataset is an administrative data managed by the Ministry of Economy. It covers all formally employed wage earners, either public or private, and is collected annually, including data on demographics, income, occupation, nationalities, new hires and terminations during the year. In 2019, it contains information about 28,910 Venezuelans with about 19,746 employed in the formal sector as of December 31, 2019.

_Cadastro Unico_ is a database that collects details about low-income families and is used to identify vulnerable people in the society to develop appropriate benefits for them. Apart from income, it contains information on beneficiary status of _Bolsa Familia_ program, living conditions, demographics, education and labor market outcomes. This paper uses _Cadastro Unico_ of December 2017, December 2018, December 2019 and July 2020 for our analysis. On average, _Cadastro Unico_ includes information on about 78 million people (28 million households) and as of September 2020, there were about 77,291 Venezuelans (30,500 households) registered in it.

The SISMIGRA is an administrative record, maintained by the Federal Police, of migrants, who applied for residence permits and contains information on age, sex, country of birth and municipality 17
# **METHODOLOGY**

The Global Findex indicators measure the use of financial services, which is distinct from access to financial services. _Access_ most often refers to the supply of services, while use is determined by demand as well as supply.<sup>1</sup> _Use_ refers to the levels and patterns of use of different financial services among different groups, such as poor people, youth, and women.

## **Indicators**

The first set of indicators focuses on formal accounts; the mechanics of the use of these accounts (frequency of use, mode of access); the purpose of these accounts (personal or business, receipt of payments from work, government, or family); barriers to account use; and alternatives to formal accounts (mobile money).

The account penetration indicator measures individual or joint ownership of formal accounts—accounts at a formal financial institution such as a bank, credit union, cooperative, post office, or microfinance institution. It includes those who report having a debit or ATM card tied to an account.

Indicators relating to the receipt of payments measure the use of formal accounts to receive wages (payments for work or from selling goods), payments or money from the government, and family remittances (money from family members living elsewhere).

The second set of indicators focuses on savings behavior. This relates to the use of accounts, as people often save at formal financial institutions. Other indicators explore the use of community-based savings methods and the prevalence of savings goals.

The third set focuses on sources of borrowing (formal and informal); purposes of borrowing (mortgage, emergency or health purposes, and the like); and use of credit cards. The fourth focuses on use of insurance products for health care and agriculture. (See the questionnaire for the survey questions.)<sup>2</sup>

## **Data coverage**

The Global Findex indicators are drawn from survey data collected over the 2011 calendar
3.4 General Problems of economic evaluation Given such valuation problems, most economic evaluations of health programmes, projects or interventions are of cost-effectiveness rather then cost-benefits. As a result investment in health is not generally evaluated in relation to investment in other sectors, neither are investmDnts in different health interventions compared. Instead the cost-effectiveness of different strategies of achieving one specific objective are evaluated. Through such analysis, the least cost method of achieving the objective can be determined and/or the method which maximises the effectiveness of a given expenditure can be selected. Whilst CEA has an important role to play in improving the efficiency of health investments, it cannot answer wider questions about<sup>the</sup> relative importance of investments in health or even of investing in different parts of the health sector.

Attempts, moreover, to make international comparisons of economic evaluation studies should be treated with caution. Each evaluation is based on considerations specific to the country, and<sup>even Xthe</sup> community, in which it has been undertaken and so the results cannot be generalised. Programme costs, for example, will vary between settings depending on the organisation and scale of the programme, project or intervention, whilst the effectiveness of a programme will vary with the social, cultural and economic setting - affecting its acceptability and its benefits. Only, for example, by repeated studies of the same management strategies for the same intervention could general conclusions about the possible appropriateness of those strategies be drawn. (Such evaluation has been undertaken for immunisation programmes, one of the few cases where there is quite a strong link between intervention and health outcome - but the evaluations have, still, often used health service outcomes (eg.numbers of immunised children) as their focus.) 20