rafmacalaba/data-use-mentions-extended huggingface dataset:
supervision time on a variety of project-level performance indicators. In a very recent paper, by analyzing project-level variables, Denizer, Kaufman and Kraay (2011) find out that micro variables such as project early-warning indicators, supervision and evaluation lags are all significantly correlated with project outcomes. Another recent work by Fardoust and Flanagan (2011) analyzes the relation between project outcome and the impact of the analytical works developed by the World Bank.

Among the documents and data prepared by this department there is the “Historical Record of the IEG Project Ratings”, a database of ratings that IEG has been yearly collecting since 1977. This contains around 9500 projects for several evaluation variables<sup>6</sup> , among which there are the ones<sup>7</sup> we are interested in:

- IEG Outcome (from now on Outcome) - it is defined as the extent to which the operation’s major relevant objectives were achieved efficiently.

- IEG Overall Borrower Performance (Borrower) - it measures the degree to which the borrower (including the government and implementing agency or agencies) ensured quality of preparation and implementation, and complied with covenants and agreements, toward the achievement of development outcomes.

- IEG Overall Bank Performance (Bank) – it embodies the level to which services provided by the Bank ensured quality at entry of the operation and supported effective implementation through appropriate supervision.

All of these are rated with a multidimensional index, which takes into account different characteristics<sup>8</sup> . Each rating results in a six scale measure (which goes from highly unsuccessful to highly successful)<sup>9</sup> which may be interpreted for econometric purposes as a binary measure

> 6  Refer to Annex B for a detailed explanation.

> 7 A more rigorous description of the IEG variables can be found at the following links <u>http://web.worldbank.org/external/default/main?theSitePK=1324361&pagePK=64253958&contentMDK=
countries were withdrawn in single domains due to technical issues. Thus, the results cannot be simply compared across time to analyze achievement trends, and we propose a regression framework adjusting for time-invariant country characteristics and for country-specific time trends.

PISA data also include detailed characteristics of students and their schools. We use information on gender, socioeconomic status, and immigrant background to explain differences in achievement scores, but also to control for possible changes in student samples across time. The PISA measure of socio-economic status (ESCS) is an index measuring student family economic, social, and cultural status. We use scores that were equated to 2015 to make them comparable across cycles. Descriptive statistics for these variables are presented in Annex Table A1. In the regressions, we use student-level variables as typical control variables but also country averages for every cycle to adjust for sample and population changes over time.

Finally, we use information from students and principals to estimate length of school closures. In 2022, students and principals were asked for how long their schools were closed due to COVID-19. We recoded their responses to the number of weeks of school closures (see Annex Table A1). We also merge PISA data with information from UNESCO (2023b) on the total number of weeks schools were closed fully or partially. Since the pandemic began, UNESCO monitored school closures and modalities for delivery around the globe daily. Schools were considered fully closed in case of “government-mandated closures of educational institutions affecting most or all of the student population.” On the other hand, schools were considered partially open when: (a) open in certain regions and closed in others; and/or (b) open for some grades, levels, or age groups and closed for others; and/or (c) open with reduced in-person class time, combined with distance learning (UNESCO 2023c).

Descriptive statistics for the four measures of the length of school closures caused by COVID-19 are provided in Table 1, while Annex Table A2 provides exact values on each
easily and discrepancies investigated. These databases have then become important tools of banks’ reserving policies.

These types of public credit registries are also an extremely useful source of information to estimate probabilities of default and other parameters to feed in to credit risk models. For each of the techniques described above, combined with other assumptions (e.g.: on underlying correlations and on loss given

> 47 See Falkenheim and Powell (2003), Balzarotti, Falkenheim and Powell (2002), Balzarotti, Castro and Powell (2003) all on historical Argentine data and Marquez (2002) on Mexican data.  See also xxxx for a very similar study on Italian data.

> 48 Such models include Creditrisk+ (often referred to as an actuarial model and focused more on risk than on pricing, developed by Credit Suisse Financial Products a part of Credit Suisse First Boston) and Creditmetrics (developed by JP Morgan offshoot, Riskmetrics, perhaps simpler conceptually than Creditrisk+ but more taxing in terms of data and focused on pricing as well as on risk).

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06|0.11|0.00|0.01|
|Dept. of Resettlement<br>0.10|0.06|0.65|0.00|0.01|
|Ministry of Agriculture<br>0.02|0.04|0.06|0.00|0|
|Local Government<br>0.01|0.01|0.01|0.00|0|
|Inherited<br>0.21|0.22|0.06|0.17|0.45|
|Purchased<br>0.06|0.16|0.03|0.03|0.12|
|Don't know<br>0.01|0.01|0.00|0.00|0.02|
|Number of farms<br>4,037|539|562|2,212|724|

_Source:_ Own computation from 2020 Zambia Eastern Province Farm Survey. Information from owner respondents only. _Note:_ There are 14 registered blocks with a total area of 231,758 ha sub-divided into 2,688 digitized parcels and 35 digitized isolated fragments with a total area of 43,866 ha.

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_Services PTAs_ setting, according to the estimation approach described here below.

# **4 Methodology**

## **4.1 Estimation strategy**

We focus on the direct effect of PTAs on services trade and services VA sourcing within a standard structural gravity estimation framework.<sup>11</sup> Hence, we obtain partial effects of PTA provisions on trade that should be interpreted as reduced form estimates.<sup>12</sup> .

The dyadic country-pair-year structure of our trade and value-added datasets, alongside a large number of countries, makes it possible to adopt a state-of-the-art approach based on the following estimable equation: where _Yij,t_ is either the value of directional bilateral services exports<sup>13</sup> between countries _i_ and _j_ in year _t_ , or the VA ratios _sh_ _~~s~~ ervV A_ _~~s~~ ervij,t_ and _sh_ _~~s~~ ervV A_ _~~t~~ ot_ _~~e~~ xpij,t_ described above. **SPolij** _,_ **t** is our key variable of interest and represents time-varying services trade policy at varying levels of aggregation. At the most aggregated level, and thereby connecting back to most of the empirical literature thus far, this variable is simply a indicator for the presence of a services PTA, additionally broken out by type of PTA (bilateral, plurilateral, or EU-bilateral).<sup>14</sup> In other specifications, **SPolij** _,_ **t** represents a vector

> 11We therefore build on the long tradition of research on trade agreements using gravity models; see Lim ̃ao (2016) for a survey of this literature.

> 12General equilibrium effects will be accounted for, and embodied in, the fixed effects structure but we do not currently exploit this information.

> 13Note that country-pairs may belong to more than one PTA (e.
program (381 in intake 4 and 362 in intake 5) out of a total of 2,213. Since the baseline screening survey was administered to all applicants, a logical first step is to verify that the baseline characteristics of program participants look similar to all eligible applicants, as would be expected if selection were indeed random.

We compare the means of several basic baseline characteristics between participating youth and other eligible youth who did not participate, and test whether the difference in means is statistically significant. These results are reported in Table 4 (A). Column 1 shows the means and standard deviations for eligible non‐treatments (or “other eligible”), while Column 2 shows the corresponding means and standard deviations for treatment youth. Column 3 indicates the difference in means (and standard errors underneath), and if the difference is statistically significant at conventional levels. The results indicate that, as expected, the two groups are very similar to each other. The only characteristic that is somewhat different (at a 5% level) is never having a formal (wage) job before, with respectively 79% of treatments and 75% of eligible non‐treatments being in this category. In terms of the levels of basic characteristics, treatments are on average 24.7 years old, 63% are males, 37% are married, and the average educational attainment is 9.3 years (see Column 2, Table 4A). 36% of them report having some earnings from an informal job/self‐employment last month, and 32% report looking for a job during the same reference period. Overall, the similarity of the two groups is consistent with the random selection of participants from the pool of eligible candidates.

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increases in the statutory tax rate. For the years of our sample, the statutory tax rate increased from 15 percent to 17 percent. After 2007, the ENIA’s panel structure is eliminated, so that firms cannot be identified across years. For this reason, we do not use data after 2007, as doing so would have limited some of our quantitative exercises.

Firm and plant level data are only available for 1996 (we do not use this year since it cannot be linked as a panel with the rest of our data). In that year, more than 95% of firms were operating in single-plant establishments. We will use the terms plant and firm interchangeably. The ENIA collects data on revenue, net accounting profit, profit tax, employment, wage bill, fixed assets, and industry among other variables useful for our quantitative analysis. Previous versions of this census have been used in many studies, given its rich plant-level data. In Chile, the manufacturing sector accounted for roughly 17 percent of value added and 14 percent of employment for the period 1998-2007. Further details on the construction and representativeness of our sample can be found in Section A of the appendix.

# **2.2 Tax Rate Facts in Chile**

In this section, we document relevant tax facts about Chile. In Chile, all firms are subject to the same statutory tax rate, regardless of their level of profits. The ENIA collects plant-level data on net accounting profits and profit tax expenses. Using these two variables, we calculate the effective tax rate that each firm faces in a given year, as the ratio between profit tax expenses and gross accounting profits.<sup>4</sup> Figure 1 shows the distribution of effective profit tax rates for 2003, which we have used as an example year, as the distributions for all the years in our sample portray similar characteristics. In particular, four important features characterize the distributions of effective profit tax rates for each year in our time-window of analysis.
micro-enterprise).<sup>3</sup> Our survey has information on a uniquely broad range of variables: sociodemographic characteristics, current and initial business characteristics, business outcomes (sales, profits and costs), managerial practices, cognitive and non-cognitive skills of the entrepreneur, access to credit, growth expectations and obstacles, among others. The sample consists of 10,275 female micro-entrepreneurs. To our knowledge, our survey is the first one measuring both cognitive and non-cognitive skills, in addition to business characteristics and outcomes, for such a large sample of micro-entrepreneurs in Mexico.

We classify the women in our sample as ”opportunity” or ”necessity” entrepreneurs according to the self-reported reason for opening their business. A respondent is classified as opportunity entrepreneur if she reports opening her business either because (i) she wanted to become independent, (ii) she had money and found a good business opportunity or (iii) she wanted to practice her profession or develop her career profile. Conversely, necessity entrepreneurs are those who started their business because they could not find a well-paid or suitable job and needed a source of income. After excluding women who started their business out of family tradition or other reasons, we are left with a sample of 8,949 entrepreneurs (21 percent classified as opportunity and 79 percent as necessity).

In Table 1, we report the mean differences between opportunity and necessity entrepreneurs in the following characteristics: (i) Business performance measures (weekly profits, sales and sales per worker); (ii) business practices, as measured by a standardized Composite Business Practices score (CBP)<sup>4</sup> ; (iii) characteristics of the business and the entrepreneur (age, proportion with at least one worker and monthly salary expenses); (iv) cognitive skills (education, standardized scores of Raven and digit span recall tests); and (v) non-cognitive skills (standardized measures of agreeableness, conscientiousness, extraversion, intellect/ imagination and neuroticism<sup
Our data is subject to some caveats. First, our results on small firms are subject to the limitation that the Enterprise Surveys sample only the formal sector in each country and exclude the informal sector. Some of the developing countries in our sample have large informal sectors which implies that we are underestimating the importance of the SME sector in those countries. In addition, since the sampling frame is restricted to 5 employees or above, our results do not speak to the micro enterprises.

Second, our data is only on the continuing/surviving firms and hence we have no data on job destruction by firms which were liquidated over the sampling period. On a related note, the surveys are stratified only by industry, firm size, and geographical location and so we may not have a completely representative sample of firm ages, though the firms within the strata are randomly sampled. Hence we present all our results on firm age as being conditional on firm size.

Third, our analysis is at the establishment level and not at the firm level since the sampling unit in the Enterprise Surveys is the establishment.<sup>10</sup> While this has the advantage that our job creation measures are well defined and capture actual new jobs at the establishment rather than changes from mergers, acquisitions, and divestitures, we are not able to measure firm size accurately for multi-establishment firms. However we are helped to some extent that the establishments in our sample report whether they are part of a larger firm or whether they are stand-alone. While most of the establishments in our data are stand-alone establishments (86%) and hence can be treated as firms, for robustness, we repeat the analysis on the sub-sample of firms that report that they have a single establishment and find that all our results about relative

> 10 In the Enterprise Surveys, the establishment is defined as a physical location where business is carried out and where industrial operations take place or services are provided. In addition, an establishment must make its own financial decisions, have
# 1. Introduction

Europe is struggling to become a competitive and dynamic knowledge economy in the modern world. One key ingredient in its strategy is likely to be the investment in human capital. Although individuals invest in human capital over the whole life-cycle, more than one half of lifetime human capital is accumulated through post-school investments on the firm (Heckman, Lochner and Taber, 1998). This happens either through learning by doing or through formal on-the-job training. In spite of its importance, economists know surprisingly less about the incentives and returns to firms of investing in training compared with what they know about the individual’s returns of investing in schooling.<sup>1</sup> Similarly, the study of firm investments in physical capital is much more developed than the study of firm investments in human capital, even though the latter may well be at least as important as the former in modern economies. In this paper we estimate the internal rate of return of firm investments in human capital. We use a census of large manufacturing firms in Portugal between 1995 and 1999 with detailed information on investments in training, its costs, and several firm characteristics.<sup>2</sup> Most of the empirical work to date has focused on obtaining estimates of the return to training for workers using data on wages (e.g., Bartel, 1995, Arulampalam, Booth and Elias, 1997, Mincer, 1989, Frazis and Loewenstein, 2005). Even though this exercise is very useful, it has important drawbacks (e.g., Pischke, 2005). For example, with imperfect labor markets

> 1An important part of the lifelong learning strategies are the public training programs. There is much more evidence about the effectiveness (or lack of it) of such programs compared with the available evidence on the effectiveness of the private on-the-job training.

> 2We will consider only formal training programs and abstract from the fact that formal and informal training could be very correlated. This is a weakness of
and Pierola (2016) argue that in lower-income countries informational failures lead to wasteful entry into export markets by many non-resilient firms that need to experiment in export markets because that is the only way for them to deal with uncertainty about the profitability of their exports and uncertainty on the part of buyers. Our results indicate that in Argentina export costs are comparatively larger than in similar countries, which leads to many resilient and non-resilient firms never experimenting in export markets.

We also find that being a direct importer and importing more intermediate inputs is positively and significantly correlated to export growth, export diversification (in terms of number of destinations), and export quality. On average, an exporter that directly imports intermediate products has 37.5 percent more exports and 50.9 percent higher export growth, and reaches 1.6 times the number of markets than exporters that do not import directly. More generally, we find that the use of imported inputs is positively associated with firm-level productivity. We are aware of concerns about potential reverse causality in these relationships. However, we do not attempt to determine causality but rather provide evidence about the positive relationship between access to imported inputs and export performance at the firm level.

The remainder of the paper is structured as follows. The next section describes the data. Section 3 presents the benchmarking exercise of export characteristics and dynamics in Argentina. Section 4 estimates the export premiums for exporters-importers. Section 5 concludes.

# **2 Data and Descriptive Statistics**

Our analysis makes use of the transaction-level export and import raw data from Argentina’s General Customs Bureau (Dirección General de Aduanas, DGA). The database covers the entire universe of transactions between 1994 and 2018 and contains unique and time-consistent firm identifiers that allows us to merge export and import transactions at the firm level. We collapse exports and imports at the firm-year level, preserving the following variables: exported/imported 4
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Transportation networks and proximlty to rural towns appears to contributo to the much higher Asian incro ental consumption on rurallyproduced goods and services. A comparison of the African consumption studies reveals that households ln Zaria Region, Nigeria spend a significantly greater portion of their income on nonfoods, especially servlces, than do consumers in Sierra Leone and in Gusau. This difference arises, at least in part, because Simmons (1976b) dellberately selected her three Zaria study villages for their proximity to ths urban center of Zaria. Consequently consumers in the study villages had easy access to a wider range of consumer goods and services than are available in most rural African villages. Contrasting the Gusau and Nuda studies reinforces this conclusion. In Mudt, with its dense population and sx¢ellent transportation system, consumers spend larger income increments on rurally-produced nonfarm goods and services than do households in Gusau where villages are typically much more removed from market centers and are faced with very limited means of transport.

The Sierra Leone study (King and Byerlee, 1977 and 1978) also supports two themes that emerged from the time-series and locality-size employment data. First, it indicates that consumption linkages are much stronger for rural services than for rurally-manufactured goods. Marginal budget shares for rural services stand at 11.2 percent while those for rurally-manufactured goods reach only 1.1 percent. Second, King and Byerlee's results underline the Importance of rural towns. Their data indicate that consumers allocate 5.5 percent or incremental expenditure on goods and services produced in rural towns, almost triple the rate of spending on products made in large urban areas. F. Caution The available evidence examined thus far has suggested that African farm-nonfarm linkages may be lower than those found in Asia.
expected feedback from the government does not work to boost support for the authorities. On the contrary, as the case of San Diego reveals, if roads are bad, the incumbent is punished. Furthermore, our findings show that each pothole complaint increases the number of votes for the incumbent by a considerably larger amount than one vote (specifically, the boost is anywhere between 29-44 votes), thereby implying that the causal mechanism is driven by something other than the impact on the individual who has engaged with the government. We were able to further clarify this issue by using the available data.

We have already noted earlier the efficiency of road maintenance systems in Moscow in terms of the time allocated for fixing the revealed problems. An additional test of the importance of

> 21 - <u>https://bftcom.com/expert bft/8964/ [last accessed: September 2020].</u>

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# 9 Appendix: Baxter-King filter

The best way of detrending macroeconomic time series data has proved to be a longlasting and still unsettled matter (see, for example, Guay and St.-Amant, 2005). In the text, we have used the most popular filter, Hodrick-Prescott, which is based on decomposing a time series into permanent and transitory components. The most common alternative filter is surely Baxter and King (1999), which is based on isolating certain business fluctuations in the data. This appendix suggests that the main message behind our key stylized facts is robust to using the Hodrick-Prescott filter.<sup>57</sup> Table A2 reports the correlations that follow from replicating Figures 1, 2, 4, and 5 for the case of the Baxter and King filter. The table indicates that our three main stylized facts continue to hold. First, EMDE are more procyclical than AE (0.26 versus -0.19, respectively). Second, commodity exporters are more procyclical than non-commodity exporters (0.27 versus 0.07. with the latter not significantly different from zero.) Third, while procyclicality in both AE and EMDE has fallen since 2006, it has fallen more in non-CE. Indeed, a comparison of Columns 4 and 5 in Table A2 shows that procyclicality in CE has fallen from 0.28 to 0.20 but, in non-CE, acyclicality has turned into significant countercyclicality.

> 57Kaminsky et al. (2004) and Carneiro and Garrido (2016) also test robustness using the Baxter-King filter. In addition, the latter compute fiscal cyclicality indicators using the Christiano-Fitzgerald and Butterworth filters and the Harvey unobserved component model. All key results are unaffected.

37