rafmacalaba/data-use-mentions-extended huggingface dataset:
which by themselves are neither specifically designed for this complex process nor to collect sufficient independent information. This ends up creating a confusing and dissuasive business environment. It is enough to think that before Autostrade was given the right to obtain a tariff adjustment for the difference between the expected and realized inflation rate, two years and some six authorities were involved in the decision process (ANAS, Consiglio di Stato<sup>36</sup> , Autorità per la vigilanza sui contratti pubblici di lavori, servizi e forniture, NARS, MEF and MIT<sup>37</sup> ) before a decisive statement by the Consiglio di Stato solved the situation.

34 The reports states, in Italian, that “La spesa per investimenti, nelle more delle approvazioni dei progetti di bilancio per l’anno 2008, sostenuta dalle società concessionarie autostradali per investimenti, rilevata dai dati di contabilità analitica al 31 dicembre 2008, è risultata pari a 1.674 €/Milioni (escluso CAS), a fronte di una previsione di piano di 1.977 €/milioni corrispondente all’86percent,” refer to ANAS 2008 reports, available at <u>http://www.stradeanas.it/index.php?/content/index/arg/dati_di_bilancio</u> 35 For these data, please refer to the section bilanci in the Atlantia website. Data for 2009 are not ready yet. <u>http://www.atlantia.it/it/bilanci/2006.html</u> 36 <u>http://ricerca.repubblica.it/repubblica/archivio/repubblica/2002/11/21/autostrade-vince-la-partita-delle-tarifferecuperera.html</u> 37 Please refer to Autorità per la vigilanza sui contratti pubblici di lavori, servizi e forniture, Relazione 2007, Pag 172, 176, 177.

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industries with i, the specification takes the form: The set D contains three distance bands with respect to the GQ network: a nodal district, 0-10 km from the GQ network, and 10-50 km from the GQ network. The excluded category includes districts more than 50 km from the GQ network. The f3j coefficients measure by distance band the average change in outcome Yd,i over the 1999-2009 period compared to the reference category.

We consider two outcome variables Yd,i expressed in logs: log loan counts and log average loan size. District x industry cells are only included if they have measured loan activity in both periods. All estimations control for industry fixed effects Ti, which is equivalent to including industry-x-year fixed effects in a panel regression. We thus control in our estimations for any industry-wide changes in loan activity, due for example to a growth or decline in sector activity or financial dependency. Regressions further control for the baseline level of financial development of each district to flexibly capture issues related to economic convergence across districts.

Estimations include 12,225 observations and are weighted by the log population of the district recorded in the 2001 population census, the year prior to the implementation of GQ. We cluster standard errors by district. We winsorize outcome variables at the 1%/99% level to guard against outliers.

# 4.2 Baseline Estimations

Table 1 reports the core results with specification (1). The dependent variable for Columns 1 and 2 is the change in log loan counts for a district-industry over the 10-year period; the dependent variable in Columns 3 and 4 is the change in log average loan size.

Regressions in Columns 2 and 4 further add state fixed effects, which is equivalent to including state-x-year fixed effects in a panel regression. This is a much more aggressive empirical approach than the baseline estimation as the augmented regression only considers variation within states (and thus we need to have districts located on the GQ network and those farther away together in individual states). By validating our results with the
the absence of the program.<sup>5</sup> While fifth graders in the treatment group did better in math and language tests than those in the control group, particularly in rural areas, program effects for ninth graders in both subjects are negative. Yet, the validity of the findings of this paper is limited for at least two reasons. First, if program effects do exist, they are probably difficult to identify due to the lack of enough statistical power in their analysis. Sample sizes for the nonparametric models of the paper are very low, ranging from 100 to 300 observations depending on the age groups. Second, and perhaps more troublesome, the nationally administered tests used by the authors to infer the academic performance of children (known as ― _Pruebas Saber_ ‖) are only representative at the school rather than at the individual level.<sup>6</sup> This paper seeks to contribute to the understanding of the effects of CCTs on school completion and learning outcomes by early adulthood. In particular, this study adds to the existing literature in three ways. First, the analysis focuses on the dynamics of program impacts in the long run as it tracks different cohorts of treatment children who have been in the program from one to nine years. Most of the few studies that measure program effects on intermediate and final outcomes in education did so with children who have been exposed to the treatment for no longer than two years. Second, we use three different data sources and samples (the baseline survey of the evaluation of the program from 2002, the program‘s information system, and a census of poor households) to perform two different methodological approaches which allows for a comparison of the findings across methodologies. Additionally, by using data from the program‘s information system for impact evaluation purposes, this paper highlights the opportunities for research that may arise from using monitoring and evaluation systems, as these are becoming increasingly popular tools to administer CCTs and other safety net programs. Finally, this
in the evaluation. Instead, the existence of external interventions which may have changed conditions in the comparison villages would only make it clearer why it is essential to have baseline data at those sites.<sup>22</sup>

> 22 In describing the details of how outcomes at the MV sites will be compared to those from the comparison villages, the protocol says that “[f]or variables where no data on baseline status exist, rural sub-national data will be imputed.” It is not clear how this imputation will work, and imputed baseline figures cannot substitute for true baseline data.

22
non-linear form. This result may be due to the fact that decentralized levels of government are limited in their capacity and do not possess economies of scale. Further, construction is an activity that is very sensitive to geography as well as the efficiency of contracting-out arrangements. Since even countries with centralized provision of roads do use contracting out, it may be that this effect is confounded in the country specific effects and cannot be generalized at a crosscountry level. The case studies on Korea and Germany in the following sections demonstrate the country-independent effect of decentralization on unit costs of construction.

When the impact of various types of decentralization on preference costs using the cross-country panel is considered independently (model 2), the results show that only decentralization of maintenance bears a significant effect (decentralization of construction and administration are not significant). The coefficients with respect to maintenance are significant and negative initially (first order effect), implying that condition of roads improves with increasing degrees of decentralization. The second order effect for decentralized maintenance costs is positive and significant, indicating that the relationship between preference costs and maintenance decentralization is convex, _25_
- [48] Parry, I., D. Heine, S. Li, and E. Lis (2014): Getting Energy Prices Right: From Principle to Practice. Washington, DC: International Monetary Fund.

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report, which assesses the quality of public services provided by the state in six countries in Latin America, as well as the citizens’ degree of satisfaction (Pareja et al. 2016).

_Living Life_ attempts to establish measures of bureaucratic efficiency, one factor among several that can prevent a country from progressing in key social outcome indicators as measured by the UN Human Development Index, the Organization for Economic Co-operation and Development’s (OECD) _How’s Life_ report, the European Commission’s Sustainable Development Indicators, or the Social Progress Imperative’s Social Progress Index. Inefficient bureaucratic procedures may also hinder the effective realization of key democratic principles assessed by the International IDEA Democratic Accountability in Service Delivery and State of Democracy data or by the Varieties of Democracy data sets. The same democratic principles recognized by these legal assessments may not be achieved in practice if the related administrative requirements are too complicated or costly.

_Living Life_ also complements four key cross-country indicators discussed in detail by Van de Walle (2005). The first set of indicators is taken from a paper published by the European Central Bank (ECB) and compares public sector efficiency covering 23 OECD countries in 1990 and 2000. A second set of indicators are taken from the World Bank’s Worldwide Governance Indicators (WGI) released every two years. A third indicator is the World Economic Forum’s annual Public Institutions Index (one of the pillars of the Global Competitiveness Index covering 117 countries). The fourth indicator is IMD Business School’s annual indicator for government efficiency covering 60 countries.

Both the ECB’s indicator and WGI overlap with _Living Life_ ’s areas of study. However, there are a few core differences. The ECB indicator measures the efficiency of government performance by comparing inputs (public spending as a share of gross domestic product (GDP)) to outcomes, while _Living Life_ measures the ease of access to those basic public services as opposed to the outcomes or the quality of those outcomes. The WGI’s Government Effectiveness Dimension
that are closely linked to the predictions generated in the previous section. These include the measures that relate to the capacity of the government to effectively formulate and implement sound policies (Government Effectiveness, Regulatory Quality) and the respect of citizens and the state for the institutions that govern economic and social interactions among them (Rule of Law, Control of Corruption). The prior discussion of elite clientelism suggests that cabinet size should be negatively associated with all four of these measures.

There is an extensive debate around the validity and methodology of the WGI (Anheier et al. 2018, Apaza 2009). Whether these measures capture distinct concepts is disputed, and Langbein and Knack (2010) conclude that they appear to measure the same broad concept. For robustness, we also use a novel corruption index from the Varieties of Democracy ( _V-Dem_ ) project that captures executive corruption and is based on expert scoring (see also Bäck et al. 2019).<sup>8</sup> Finally, we include the well-known Corruption Perceptions Index produced by Transparency International.<sup>9</sup> It would be ideal to have objective measures of corruption and governance. However, most objective measures are country specific – such as audit outcomes indicating corruption (Ferraz and Finan 2008) or amounts of funding diverted from authorized purposes (Olken 2007). Hence, perception-based measures remain important. To ensure comparability and to aid interpretation, we rescale these measures to a theoretical range from zero to 100.<sup>10</sup> We draw on the World Bank/Inter-American Development Bank’s _Database of Political Institutions_ to control for a number of underlying pressures on cabinet size identified in the preceding section. We include the partisan fractionalization of the executive, as multi-party coalitions may lead to increased cabinet size to accommodate politicians from across the different parties that participate in the government. Next, we use indicators of whether the chief executive or defense minister are military officers. A strong presence of military officers may suggest threats to
22 coordinating tax reforms: can lost tariffs be recouped?

tax rates are dropped. The cleaned data set that is ready for simulation consists of 265,194 import records spanning 4032 tariff lines from 133 economies.<sup>27</sup> The paper also incorporates domestic production data extracted from the latest quinquennial Census of Manufacturing Establishments that reports the domestic sale of manufactured goods (Government of Nepal 2008). For 3,079 of the 4032 import codes, there exists matching data for domestically sold products. This allows for substitution of imports by domestically produced goods when the price of imports rises, adding to the richness of simulation results. There are, however, two limitations. First, the latest production data are available only up to the fiscal year 2006-07, whereas the import data straddles the fiscal years of 2007-08 and 2008-09 (that is, calendar year 2008). Second, production data covers only manufacturing industries. For a little less than 25 percent of the tariff lines that belong to non-manufacturing industries, there are no data on domestic production. In the language of the model, for a subset of imports, the substitution between imports and domestically produced goods is _perfectly_ inelastic.

The 133 import trading partners of Nepal in 2008 are organized in eight groups: (1) India; (2) China, including the Tibet Autonomous Region; (3) Rest of South Asia (Afghanistan, Bangladesh, Bhutan, Maldives, Pakistan, Sri Lanka); (4) Northeast Asia (Japan, Republic of Korea, Hong Kong Special Administrative Region, and Taiwan); (5) Southeast Asia (Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam); (6) North America (Canada, Mexico, United States); (7) the European Union; and (8) the Rest of the World (ROW). The baseline scenarios assume an export substitution elasticity of 1.5, domestic substitution elasticity of 1, and import demand elasticity of 0.5.

>
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data where is the quantity of output, and are raw material, labor and capital inputs respectively, and is total factor productivity; , and is the elasticity of production with respect to and in the output function. Let and denote the price of output, the price of raw material inputs, the wage rate and the user cost of capital respectively. We rewrite the production function in terms of values since in our empirical analysis only data on the value of sales, raw materials, and capital is available. Further taking the natural logarithmic transformation, we obtain: <u>p</u> where , S pY is the value of sales<sup>14</sup> and v p v w v r denote the p w r values of raw materials, labor costs, and capital respectively. Hence, the intercept reflects the impact of prices and productivity on the value of output.

In the short run, capital is fixed and firms maximize profits over the choices of raw material and labor inputs, taking input prices (which could be firm-specific) as given. If we substitute the profit-maximizing levels of M and L into (4), we obtain an expression for the optimal (log) sales (S ): The capital service flow variable is taken as a weighted average of the values of capital stocks such as machinery and equipment, vehicles, and buildings (Christensen and Jorgensen 1969):

> 14 We assume firms do not have inventory so that output values are equal to sales values.

13
**Figure 6: Median wages by gender & sectors**

<!-- Start of picture text --> Average (median) total monthly wage<br>Public<br>administration<br>Public<br>utilities<br>Finance &<br>Business<br>Other<br>services<br>Construction<br>Transport &<br>Communications<br>Manufacturing<br>Commerce<br>Mining<br>Agriculture<br>0 10,000 20,000 30,000 40,000<br>Female Male<br><!-- End of picture text --> Source: Staff estimates based on HIES 2016 13
Rev. 3.1 code). Of these interviews, more than 90,000 are with top managers and business owners of manufacturing firms, for which we provide TFPR estimates. Factor ratios using labor costs and revenues are provided in the associated dataset for most firms in the sample, including firms in selected services sectors covered by the ES.

To construct TFPR, we need information on sales (Y), employment (L), capital stock (K), and intermediate inputs (M). These variables are proxied using the questions available in the data. More precisely, Y is proxied by total annual sales of the establishment (variable d2); K is proxied by the replacement value of machinery, vehicles, and equipment (variable n7a); L is proxied by the total annual cost of labor (variable n2a); and M is proxied by the total annual cost of inputs (variable n2e). For value-added (VA) specifications (presented below), VA is proxied by the difference between total annual sales of the establishment (variable d2) and annual cost of inputs (variable n2e); K and L are the same as in the gross output specification (also elaborated below). The appendix presents each variable used along with the exact wording of the questions.

# **International Comparability**

All the variables used for the productivity estimation are collected in local currency units (LCUs), which are specific to the survey and year. Consequently, the data span different fiscal years. For the estimation of cross-economy regressions all data must be transformed to a common currency-year. To do this, all variables are first converted into U.S. dollars (USD) using the official exchange rate (period average) from the World Development Indicators (WDI).<sup>2</sup> The data are then deflated to 2009 using the GDP deflator for the United States from the relevant reference fiscal year.<sup>3</sup> Note that information
- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high and low capital account restrictions. Based on the Chinn-Ito index of financial openness (where a higher number represents more financial openness), a country is classified as having high (low) capital account restrictions if its Chinn-Ito index is below (above) the median. The difference between the two averages is significant at the 5 percent level. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Sources: Authors’ calculations based on Chinn-Ito index and WEO (IMF) data. Commodity classification from World Bank.
institutional features that are central to developing countries such as part-time employment, unemployment with advanced education and vulnerable employment.

Our final objective is to use our Skills Index to analyze the relationship between human capital, skills and economic growth and to provide estimates of what skills enhancement can mean for economic growth. Many studies have explored the relative contribution of human capital to cross-country income differences, but the results have been inconclusive. Angrist et al (2021, p. 407) point out that _“The challenges in measuring quality have contributed to substantial variation in estimates of the role of human capital in cross-country differences in income,_ **_ranging from nearly all to potentially none_** _” (our emphasis)._ Part of the reason for this is due to the significant heterogeneity of the countries considered. Estimates indicate that while human capital accounts for 54% of cross-country income differences in advanced economies, human capital accounts for only 4% in Sub-Saharan Africa.

In our paper, we focus on a similar group of developing countries in Asia. We develop a composite Asian Skills Index (ASI) for this group and then use the ASI as a proxy for human capital to study the link between skills development and economic growth. The ASI represents an extension of the effort initiated by the ESI to Asian countries. An important motivation for this effort is the concern that COVID may have on skills acquisition and the risk of large cohorts of children leaving school with few work-related skills. The index also may highlight the deleterious effects of COVID on the current operation of labor markets and the worsening imbalances of supply-demand mechanics in labor markets.

We apply a clustering algorithm using “K-means” to identify a similar group of Asian developing countries. For this group, which includes countries like Sri Lanka and Pakistan, we 7
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