rafmacalaba/data-use-mentions-extended huggingface dataset:
In some cases, PCRs impose a floor for the credits that are to be reported. This leaves unregistered a large number of borrowers that account for a small share of bank credit. While these floors prevent the databases from ballooning, by systematically excluding small debt data (e.g. microcredit data) they impose a censoring on the database that may undermine the reliability of some of the analysis performed. In the end, operators of the PCR need to strike a balance between the advantages of a complete picture of the credit market with the challenge that an enlarged PCR may impose (e.g. the potentially higher marginal cost and time implications of collecting and managing smaller credit information by both the banks<sup>34</sup> and regulators/supervisors).

Given the need to strike a balance, PCRs can outweigh such costs undertaken by banks by providing them with credit information that can facilitate their credit risk management practices. This is of particular importance when PCRs are the only source of positive information in the credit information market, since in some countries private credit bureaus are nonexistent or unable to obtain and distribute positive information. In these cases, by downscaling PCRs, lower income borrowers are able to demonstrate their payment behavior.

# **VII. Concluding remarks**

Much of the recent literature in banking and financial stability topics has addressed the causes of the subprime crisis. In some cases, the literature highlights the flaws and loopholes of rigid regulations and supervisory processes that failed to react to the imbalances and vulnerabilities as they built up in the preface of the crisis. Other literature stresses the psychological drivers of the subprime crisis; analyzing how borrowers embarked into a borrowing frenzy fueled by myopic investors and insufficient knowledge of ABSs and CDOs. Once the crisis was rolling, the panic in the market place also played a prominent role. In all these factors some sort of informational vacuum was present: the extent to which households had overstretched their repayment capacity and their exposure to an upside in interest rates, banks
production function and earning equations as in Hellerstein, Neumark, and Troske (1999).

The information on individual establishments that INEGI gathers through its questionnaires (which firms are required to answer by law) is legally confidential. Therefore, we followed an established procedure in which most data analysis was done at INEGI’s Aguascalientes headquarters with the support of INEGI personnel. Nevertheless, the reader should bear in mind the limitations for data analysis imposed by this institutional arrangement.

# **III. WORKERS’ AND FIRMS’ CHARACTERISTICS**

The observation unit in ENESTYC is the firm, and includes detailed information on the characteristics of its workforce. The workers’ characteristics included human capital characteristics: education, experience, and training; and gender, whether the employee belongs to a union and the type of contract. We used the firm-level data, the shares of the workforce with these attributes.

The training analysis focuses on _formal_ training only, which is the training provided by ‘training professionals.’ Formal in-house training courses are given by specialized firm personnel (not peers), while external training describes formal training courses given by professional external agents, which is mostly provided by private institutions.

The distribution of workers’ characteristics is tabulated, with sampling weights, in Table 1. We find the percentage of women employees is significantly less than men, with men representing nearly 73 percent of the workforce. With respect to educational attainment, 71 percent of the employees had no more than lower secondary education, while 19 percent of them received upper secondary education and the remaining 10 percent of employees received university-level education. The majority of workers in these firms (48 percent) had less than three years of experience in the firm, while only 15 percent had worked more than ten years in that firm.

5
Table 1: Descriptive statistics of the analytical sample

||||Survey Wav<br>|e<br>|
|---|---|---|---|---|
||Pooled|NRVA 2007|ALCS 2014|IEL 2020|
||(1)|(2)|(3)|(4)|
|Annualized income (USD)|1891.25|1241.84|2276.80|1952.63|
||(1390.15)|(976.44)|(1532.45)|(1259.11)|
|Female|0.10<br>|0.14<br>|0.08<br>|0.08<br>|
||(0.30)|(0.34)|(0.28)|(0.27)|
|Experience|18.81|21.10|18.44|16.93|
||(14.04)|(13.92)|(14.12)|(13.69)|
|Experience square|551<br>|638.66<br>|539.41<br>|473.99<br>|
||(654.69)<br>|(683.36)<br>|(650.14)<br>|(618.06)<br>|
|Public sector|0.66|0.67|0.67|0.64|
||(0.47)|(0.47)|(0.47)|(0.48)|
|Age|34.07<br>|35.90<br>|33.37<br>|33.24<br>|
||(11.82)|(12.21
employed in the service sector. Most of the workers left the country over the last few years, during which period real wages also grew on average by more than 20 percent.<sup>6</sup> Anecdotal and theoretical evidence suggests that emigration-induced shock to labor supply could be a driving force behind this rapid wage increase.

The paper offers a number of contributions to the literature. First, it provides empirical evidence on the impact of emigration on source country wages outside the North American experience. Second, it makes use of a unique database on emigrants, documented by a nationally representative survey conducted in the source country. Third, for the reasons noted above, on the size of emigration, Moldova offers an ideal case to study the impact of emigration on source country wages. In addition, Moldova also represents what is arguably a more typical source country, with its migrant workers spread out across multiple host countries rather than residing in a single host country, as in the case in North America. The emigrant flow is thus diversified across a number of host countries, and though the majority of workers are in Russia, Moldova’s remittance inflows are not as exposed to country-specific downturns. Fourth, this study is unique in that it combines the previously studied net labor supply shock due to emigration with information on sector specific labor demand shocks.

The rest of the paper is organized as follows. Section II provides a simple and stylized theoretical introduction while Section III introduces empirical framework and some descriptive evidence. We then discuss empirical findings and robustness issues in section IV, which is followed by a concluding remark in section V.

# **II. A simple analytical framework**

The textbook model of a competitive labor market yields clear and unambiguous implications of a migration induced reduction in labor supply. Ceteris paribus, a reduction in the supply of labor outflows because of migration should increase the wage

> 6 Close to 80 percent of all emigrants left Moldova between 2000 and 2006. At the end of the 1990s,
construction of pre-tax income distributions in the survey and tax administrative records, and include more details in the appendices.

### **4.2.1 Income in survey data**

We use survey micro-data to compute individual income as the sum of four components: wages, mixed income, capital income and imputed rent. Wages include salaried work from all activities, including additional benefits for formal employees and wages from informal employment. We also include in the wage component all pensions and

> 8For a detailed discussion on how the WID approach differs from another initiative to bridge the gap between micro- and macro-data on inequality, the OECD-Eurostat Expert Group on Disparities in National Accounts (EG DNA), see Zwijnenburg (2019).

8
*Cash Fl**|**ow Forecasting and Cash Balance Management**|
||1. Effective Cash Flow Forecasting|
|DPI-11|2. Effective Cash Balance Management|
|**Operati**|**onal Risk Management**|
||1. Debt Administration: AvailabilityandQualityof Documented Procedures for Debt Service|
|DPI12|2. Debt Administration: Availability and Quality of Documented Procedures for Data<br>Recordingand Storage|
|-|3. Data Security: Availability and Quality of Documented Procedures for Data Recording and<br>System and Access Control|
||4. Data Security: Frequencyof Back-Ups and Securityof Storage|
||1. Segregation of Duties|
|DPI-13|2. Staff Capacityand Human Resource Management|
||3. Operational Risk Management,Business Continuity,and Disaster RecoveryPlans|
|**Debt Re**|**cords and Reporting**|
||1. Debt Records: Completeness and Timeliness|
|DPI-14|<br>2. Debt Records: RegistrySystem|
||1. Central Government Debt Data: Statutoryand MandatoryReportingRequirements|
|DPI-15|2. Public Sector Debt Data: Statutoryand MandatoryReportingRequirements|
||3. Debt Statistical Bulletin:Qualityand Timeliness|

29
6

# **4. Motorization**

What have analysts discovered about the determinants of motor vehicle ownership? Because vehicles are mainly privately produced and purchased, vehicle ownership is usually analyzed in the traditional economic demand framework. Knowledge about the effects of income, prices, demographic trends, and transport policy on car ownership is needed to predict the effect that specific policies might have on vehicle ownership patterns.

During the last four decades, many empirical studies have modeled and forecast the trends of motor vehicle ownership and use in various countries.<sup>7</sup> These studies fall into three categories, based on the type of data used. In the first category are models that analyze motorization using time series data (trends over time in a city or country) or attempt to extrapolate national or regional motor vehicle ownership trends to future years under the explicit assumption of a saturation level.8 The second category of models uses cross-section data (data from a single point in time where variations are analyzed across households, cities, or countries). Some of these studies use aggregate data to produce longrun income elasticity estimates while others use disaggregate household level data to explain household car ownership behavior.<sup>9</sup> The third category of models uses panel data, 7For a concise survey of the commonly used methodologies, see Juan de Dios Ortuzar and Luis G. Willumsen, _Modeling Transport_ (New York: John Wiley & Sons, 1994), pp. 405-13. For more thorough discussions of the econometric techniques, see Stephen Glaister, _Fundamentals of Transport Economics_ (New York: St. Martin's Press, 1981); Kenneth Button, A. D. Pearnan, and A. S. Fowkes, _Car Ownership Modeling and Forecasting_ (London: Gower Publishing Company Ltd., 1982); M. J. H. Mogridge, _The Car Market: A Study of the Statics and Dynamics of Supply-Demand Equilibrium_ (London: Pion, 1983); and Kenneth E. Train, _Qualitative
∗∗_</sup> _p <_ 0 _._ 001.

For the local calibration, the impact of the policy as a percent change is estimated as 23%, which is very similar to the 25% estimated with the data from the regulatory-grade monitors and 24% with the satellite data. The coefficient (treatment effect in levels) estimated using the satellite calibration is very similar to the one using the regulatory monitor calibration, though the percent change is smaller at only 18%.

We conduct several robustness checks. Using data from only 2020 and 2023, we compare the results across the three data sources at the daily level using both the maximum hourly PM2.5 value of the day and the average of the hourly values for the day (Table A3 Panels A and B). Since there is only one year of comparison and therefore a small number of observations when conducting the analysis at the daily level, the results are much less stable. For both outcome variables, the coefficient remains negative when using data from the regulatory-grade monitor, but it is no longer significant and the percent change is much smaller. Using the estimates from satellite data, the coefficient is positive and not significant. Using the low-cost data, the results remain negative and significant when the outcome variable is the daily average, though the percent change is smaller. We conduct an additional robustness check, adding more years to the analysis.<sup>23</sup> The analysis at the hourly level shows very similar results to what we find when only comparing 2020 and 2023 at the hourly level, increasing our confidence in those results (Table A3 Panel C). At the daily level, we now find the results are fully aligned with the results 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
types of labor. The reason is that as productivity of adults increase (a positive change in α ) the demand for child labor would also change, changing the wages of the children and thus potentially inducing a different level of child labor in the economy. In the specific example shown above we are restricting the technology so that there is no complementary between inputs.<sup>10</sup> Given this technology, the preferences of the parents interact with the nature of the technology in such a way that a simple restriction on preferences (similar to the one described above) would give us the result.<sup>11</sup> The result obtained in Basu and Van (1998) is consistent with ours, for the condition on preferences holds trivially in the case in which parents do not value leisure. The nature of unskilled labor markets in the Mexican rural setting allows us to test for this effect using the PROGRESA/OPORTUNIDADES database.

The nature of the available technology is thus a key determinant of child labor force participation in this simple static setting. Actually, it plays an even more important role than preferences of the parents, which under mild conditions would result in reduction of child labor as adult wages increase, even for parents who value their own leisure. Let us analyze in the following section whether this result holds in a dynamic setting.

# **3.2 Dynamic Setting with Purely Self-interested Parents**

The existence of child labor has dynamic economic implications, like those in terms of human capital investment and the evolution of aggregate human capital, with an effect on the growth possibilities for the economy as a whole. We follow the unitary model of the household. Jacoby and Skoufias (1997) used a dynamic setting, as they were interested in analyzing the use of children as a pool of assets as a consumption smoothing device in the absence of perfect credit and insurance markets. In that model it is assumed that parents care about the schooling of their children as such, in
The paper can also be read as a cautionary note against knowledge management that is based on counting analytical tasks. For quite some time, it has been a common practice at the World Bank to try to align the number of active ASA budget codes with some “capacity indicator” established in a relatively crude way, based on what was delivered in previous years. In an institution like the World Bank there are simply too many analytical products underway at any point in time for this to be an effective approach, especially when a large share of such products is not even “visible” through information systems.

Instead, the methodology and findings of the paper can be seen as a call for a different knowledge management approach, one that relies on stronger information systems, a better alignment of incentives, and regular monitoring and evaluation based on statistical analysis.

Starting with information systems, a single identifier for analytical tasks would allow connecting data on all the resources they use, all the outputs they deliver, all the downloads they receive, and all the feedback from internal and external clients. Budget identifiers (P-codes) are a practical way to connect information that is currently scattered across various disconnected systems. At present, no TF can be created in the World Bank without a link to a P-code. Arguably, it should be the same for every document entered into the IB and OKR digital platforms.

The process could be hardwired, facilitating an automatic linking of the three databases used in this paper. A similar approach could be used to connect budget codes with the user feedback collected from country engagement surveys. Proceeding this way, a time will come when it will be possible to conduct data-driven studies like the one in this paper without having to go through a laborious reconciliation of entries.

On incentives, thousands of analytical tasks running in parallel call for decentralized management. But knowledge products differ in the objectives they serve, their expected outcomes, and the risks they pose to the World Bank. Ideally, the decision maker in each case
of Covid-19 on migration and household welfare in Tajikistan. Plos one 16, e0257469.

- Simpson, N.B., Sparber, C., 2020. Estimating the determinants of remittances originating from US households using CPS Data. East Econ J 46, 161–189.

- Stanley, D., Bhattacharya, R., 2008. The informal financial sector in the US: The role of remittances. Q. Rev. Econ. Finance 48, 1–21.

- Uddin, M.A., Ichihashi, M., Barua, S., 2022. Financial sector development and the preference for informal remittance channels: Evidence from bangladesh. J Dev Studies , 1–27.

- Weber, S., P ́eclat, M., 2017. A simple command to calculate travel distance and travel time. Stata J 17, 962–971.

World Bank, 2020. Covid-19 crisis through a migration lens. Migration and Development Brief, no. 32 .

16
based on patterns of economic activity. Therefore, for any area of analysis, the total sum of jobs adds up to 100% instead of representing the actual number of jobs. This has the advantage of being consistent between cities and being specifically geared to estimating informal as well as formal employment where highly localized data is not available or is inadequate.

# **Methodology**

For each residential location, we first compute the total number of locations that are reachable within 60 minutes using only public transportation or walking. For each of these reachable locations, we compute the share of total jobs that are in that location compared to the entire region. By combining the two previous steps, we are able to compute, for each residential location, the percentage of total jobs that are reachable under 60 minutes out of all jobs in the metropolitan area.

The accessibility analysis is not run at a single point in time. It is run multiple times over the course of a given window – in this case, from 8:00 AM to 9:00 AM on a Monday. The analysis tests the travel time 60 times over the window, with each minute used as a possible departure time. Destination locations will then have a different travel time at each minute-break. The proportion of times a given cell falls within the set travel time threshold (60 minutes) is calculated (in this case, using five Monte-Carlo draws per minute), and a frequency threshold is set to 50%.

This analysis requires to rely on a few assumptions that are consistent across cities. First, the walking speed is assumed to be 3.6 km/h. This speed is chosen as representing a moderate, comfortable walking pace in an urban setting. The speed of public transport does not rely on any assumptions as it is built in the transit network data that was collected.

Second, the threshold of 20 minutes is used as the maximum walking time _per leg_ of the trip. For example, an entire trip may include a
|
|Share of white-collar workers|No|No|No|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|
|FDI share|No|No|No|No|Yes|
|Time efects|No|No|No|No|No|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

40
likely be greater for changes measured over a longer horizon; and (ii) the specific period under examination because the where the economy is in the business cycle at the time will also be reflected in productivity dynamics.<sup>24</sup>

> 23 In this decomposition, the between term and the entry and exit terms involve deviations of plant-level productivity from the initial industry index (Foster et al., 2001).

> 24 For example, in Foster et al. (2001) productivity decompositions yield quite different stories for the periods that are roughly coincident with cyclical downturns. These findings suggest that these estimations should be updated as additional tax administrative data become available.

17
by or actively aligned with state service provision. Experience here has been mixed yet again, and probably more negative than positive. An early study of contracting for clinical and ancillary services in the health sector found problems created by the limited capacity of slow-moving, rule-ridden bureaucracies to perform even very basic functions such as paying contractors in a timely manner (Bennett and Mills, 1998). A more recent comprehensive survey echoed that finding, and noted that formal policy dialogue between government and non-state providers was often imperfect and unrepresentative, especially in fragile settings, and sometimes unduly dominated by the provider side (Batley and Mcloughlin, 2009).

# **The Appropriateness of NPM**

NPM has been described as ‘truly a global paradigm’ (Borins, 1997: 65) whose spread is impeded only by bureaucratic isolationism (Thompson, 1997: 13). However, the contributors to a comprehensive review in 2001 found that its incidence in developing countries was limited (McCourt and Minogue, 2001), and that picture has probably not changed a great deal in the last decade. The problem-solving approach that I have taken in this paper suggests an explanation. If NPM is essentially a response to the problem of improving performance and delivering on a government’s objectives, then a prior condition for its application must be that government makes improving performance a priority.

Whether performance is or even ought to be a priority was the subject of a debate over the application of the New Zealand version of NPM (New Zealand was a market leader in the early days of NPM). Bale and Dale (1998), as advocates, argued that the New Zealand reforms were relevant to developing countries because they were developed from a broad, system-wide perspective that focused on the causes, not the symptoms, of dysfunctionality, and because having specified the performance standards that the center expected, the reforms devolved to line agencies the management authority that they would need to meet the standards. However, they conceded that some stringent conditions were also necessary: a