rafmacalaba/datause-encoder-data huggingface dataset:
period of time. Because the data on expenditure and outcomes are not generated from the same sources and because they cover different samples of countries, one of the main and most time-consuming challenges was to define expenditure and outcome indicators in a way that would allow us to maximize the number of countries and the number of years represented in the data base. We discuss this in detail later, but it is essential to keep in mind that the binding constraint on the coverage was the availability of data on public expenditure and _matching data_ on outcomes. A country is included in the data base only if those data are available. The meagerness of this basic information is not the only issue. For a number of reasons discussed later, the exercise required some assumptions, in particular when measuring efficiency. The reassuring fact is that once all the data were processed, an interesting story emerged, even if it is a somewhat naïve story in view of the limitations just described.

The paper is organized as follows. Section 2 provides an overview of the data available on expenditure and outcomes. Section 3 discusses the main comparative facts on expenditure levels, including the main information gaps. Section 4 summarizes the information on the outcomes associated with these expenditures. Section 5 presents a rough assessment of the evolution of sector-specific efficiency levels for the four main groups of countries of concern here. The main objective of this section is to get as close as possible to benchmarking cross-country performance given the scanty data. Section 5 concludes with some suggestions for further work and discusses specific information gaps the international community will have to address in its standardized international datasets.

# **2. How much data are there, and what can you do with them?**

This section discusses the information we used to conduct large-scale cross-country and cross-sectoral performance comparisons of public expenditures. We conclude that these crosscountry assessments cannot match the quality of the analysis that could be achieved from country- or sector-specific studies conducted at the level of the business unit—central or local administration, school system, hospital, or water or electricity distribution company. Why? Because the data are simply not good enough to generate much more than “big picture” comparative assessments. The country coverage varies significantly across sectors and across time. The data are, however, good enough to give one a good sense of the evolution of the sectoral allocation of resources across broad, income-based country groupings over time.

The basic data issues can be summarized as follows. In an ideal world, we would have had access to long-term and detailed public and private expenditure data on each subsector, with matching data on all the relevant outcomes as well as all the relevant institutional variables, such as the degree of decentralization, the financing options, and other characteristics of the initial conditions with respect to the outcomes in each subsector. Those data do not exist.

4
Table 4 Regression results, OECD import concentration on time trend Herfindahl

||_(1)_|_(2)_|_(3)_|_(4)_|
|---|---|---|---|---|
|Time|-0.010|-0.009|-0.009|-0.007|
||(115.74)***|(86.33)***|(140.45)***|(92.51)***|
|Time, squared|0.000|0.000|0.000|0.000|
||(51.13)***|(27.11)***|(41.94)***|(3.42)***|
|Post 2000||0.005||0.008|
|||(14.76)***||(39.35)***|
|Constant|0.807|0.802|0.798|0.789|
||(932.13)***|(859.28)***|(1320.17)**|(1216.18)***|
|Observations|1'154'420|1'154'420|1'154'420|1'154'420|
|R-squared|0.07|0.07|0.13|0.13|
|Fixed effects a/|no|no|yes|yes|
|||Theil|||
||_(1)_|_(2)_|_(3)_|_(4)_|
|Time|-0.012|-0.009|-0.019|-0.015|
||(63.78)***|(41.27)***|(155.47)***|(100.10)***|
|Time, squared|0.000|-0.000|0.000|-0.000|
||(7.94)***|(7.32)***|(35.37)***|(7.05)***|
|Post 2000||0.012||0.020|
|||(19.11)***||(47.64)***|
|Constant|4.236|4.222|4.358|4.336|
||(2336.35)**|(2160.58)**|(3626.10)**|(3363.56)***|
|Observations|1'154'420|1'154'420|1'154'420|1'154'420|
|R-squared|0.05|0.05|0.18|0.18|
|Fixed effects a/|no|no|yes|yes|

# Number of partners

||_(1)_|_(2)_|_(3)_|_(4)_|
|---|---|---|---|---|
|Time|0.093|0.153|0.062|0.091|
||(31.30)***|(42.34)***|(40.07)***|(48.33)***|
|Time, squared|0.003|0.001|0.004|0.003|
||(46.90)***|(14.21)***|(129.70)***|(75.63)***|
|Post 2000||0.305<br>(29.10)***||0.147<br>(27.01)***|
|Constant|3.259<br>(111.72)***|2.917<br> (92.81)***|3.033<br> (196.73)***|2.871<br>(173.47)***|
|Observations|1'154'420|1'154'420|1'154'420|1'154'420|
|R-squared|0.09|0.09|0.29|0.29|
|Fixed effects a/|no|no|yes|yes|

Notes: * significant at 10%; ** significant at 5%; *** significant at 1% a/ fixed effects by importer  product pair 22
# **A.1 Example of data extraction and analysis**

In this section we exemplify how researchers can use our dataset for generating descriptive statistics about public procurement and budget execution across Brazilian municipalities. The full documentation on using datasets hosted on the Data Basis platform can be found at https://basedosdados.github.io/mais (in Portuguese), including video tutorials and details on how to create projects at Google BigQuery.

Here we provide a simple example using the ‘basedosdados‘ R package. This package allows users to connect to their BigQuery projects (and billing ID) and directly make SQL queries on BigQuery datasets, which are then loaded in R.

The SQL query presented below does the following: it connects to the _licitacao_ (tender) table including tender-level information for all municipalities and years covered in our data (over 2.3 million unique tenders); it creates an indicator for when the modality is defined as categories 8 (waiver) or 10 (non-requirement); then it computes the average share of tenders that are non-competitive and the total number of tenders at the municipality-year level. The resulting table, loaded in the ‘data‘ object, includes 19,033 municipality-year observations. When executed, this query took 3 seconds to process the 2.3 million observations and provide us these summarized statistics.

We then can simply plot a histogram of the share of non-competitive tenders across municipality - year (in this case, we filter the dataset for observations with at least 50 tenders). We present the results in Figure A.7. We can see that the average we describe in Table 2 of 30%-40% masks substantial heterogeneity: while a substantial amount of municipalities in a given year do not use non-competitive modalities, in some municipalities the share of these is above three-quarters.

Using this dataset and auxiliary tables also easily accessible in the ‘basedosdados‘ package, we could also explore additional questions, such as whether the use of non-competitive tenders vary systematically over time and whether it correlates with other municipality traits, such as GDP per capita.

42
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_Appendix 6: Econometric and analytical issues_ This appendix has five sections. Section A 6.1<sup>presents the econometric models. Section</sup> A 6.2 discusses the data and estimation problems<sup>and describes how these problems were</sup> tackled. Section A 6.3 includes the estimation results<sup>for the entire Bolivian public</sup> sector. Section A 6.4 briefly explains the construction<sup>of the IRRs for Bolivia. Section</sup> A6.5 has the IRR tables for centralized organizations.

# A 6.1. Econometric models

Econometric Model I:

**Yij=** a + b E +ei,

where Y = performance indicator, with j = aggregate performance,<sup>accountability,</sup> employee morale, and results focus; IE - institutional environment; and e = a well-behaved stochastic error term.

A linear functional form was chosen to keep the<sup>analysis simple.</sup> This model was estimated by using data points from<sup>16 Bolivian organizations. The</sup> estimation results are presented in Section 6 and in Appendix<sup>3.</sup>

# Econometric Model III:

11 where, Yj = performance-j, with j = aggregate performance, accountability<sup>(ACC),</sup> employee morale (MOR), and results-focus (RFO); X k= component-k of the institutional environment,<sup>with k=l,2, ..., 11; and</sup> **uj=** a classical error term.

In particular, xl = Existence of rules in recruitment x2 = Existence of rules in evaluation x3 = Existence of rules in training x4 = Existence of rules in recording x5 = Fair treatment 59
<!-- Start of picture text --> L=J+B6 M=K+E<br>Line of business total P & L statement costs | total P & L statement costs<br>of RI, original port. of RI, updated portf.<br>Motor vehicle liability insurance 460 1,453<br>Other motor insurance 247 461<br>Marine, aviation and transport insurance 490 fof<br>Fire and other damage to property insurance 2,702 3,129<br>General liability insurance 222 4A7<br>[Totaltt| tt<br><!-- End of picture text -->

|Line of business|L=J+B6<br>total P & L statement costs|<br>of RI, original port.|M=K+E<br> total P & L statement costs<br>of RI, updated portf.|
|---|---|---|
|Motor vehicle liability insurance|460|1,453|
|Other motor insurance|247|461|
|Marine, aviation and transport insurance|490|fof|
|Fire and other damage to property insurance|2,702|3,129|
|General liability insurance<br>|222<br><br>|4A7<br><br><br>|
|[Totalt||tt|
15 returns based on the 0-25 bins. Returns are higher but the previously discussed differences are maintained, with the corresponding returns being 4.4*** and 4.2*** for developed countries and 2.0*** and 2.8*** for developing countries (rows 14-15; N = 143122-122 and 124-98-98). These results hold if exclude female, part-time, self-employed and public sector workers (rows 16-17; N = 122-100-100 and 91-72-72).

# **4.4. Robustness Checks**

**Estimates of Returns.** Web Appx. Section A.4 and Web Appx. Table A.14 show results hold if we use country-specific ages of entry in school or drop samples exhibiting age heaping.<sup>24</sup> Teenagers and children then disproportionately work in developing economies. We may thus mismeasure experience in developing countries. This could affect our results if we under-estimate the returns of developing countries, thus wrongly asserting how large the gap in the returns is between developed and developing countries. We indeed find that including potential teen labor experience (from age 15) or potential child labor experience (from age 13) raises the returns slightly more in developing countries – from 1.7 to 1.9-2.0 (see rows 7-8 of Web Appx. Table A.14) – than in developed countries – from 3.2 to 3.4 (ibid.).<sup>25</sup> The gap in the returns then either remains the same, at 1.5, or slightly decreases to 1.3 (ibid.). If we consider teen/child labor for developing countries only, the gap is 1.3-1.2. Also controlling for cohort effects, the gaps also remain high, at 1.5-1.3 and 1.7-1.4. Overall, the returns and the developed-developing gap remain essentially similar, at least enough not to change the main implications of our results.<sup>26</sup>

Results then generally hold if we (see Web Appx. Section A.4 and Web Appx. Table A.14): (i) Restrict the sample to post-2000 samples to minimize issues arising from exUSSR countries; (ii) Use three- or seven-year bins; (iii) Keep workers for which the wage was reported for a period of at least a month since earnings may be mismeasured for workers paid hourly, daily or weekly; (iv) Include the wage of any secondary occupation (available for fewer countries); (v) Use mean wages, experience and education in the

> 24Results hold if we drop samples with a rough Whipple index. The index measures to what extent individuals disproportionately report their age as a round number ending in 0 or 5.

> 25For teen (child) labor, for individuals with at least 9 (7) years of education, experience is age - years of education - 9 (7). For individuals with less than 9 (7) years of education, experience is age - 15 (13). 26Web Appx. Figure A.4 replicates Figure 1 but add the respective wage-experience profiles of developed countries and developing countries when including teen labor (“TL” in the figure) and child labor (“CL”).
These results demonstrate that subjective questions on welfare that rely on heterogeneous scales across households might be ill-suited to precisely track how consumption and employment are changing during and after a crisis. These results suggest that in the case where a reference scale is not explicitly provided to a household, the scale itself could change following a crisis making it difficult to even identify the sign of changes in actual consumption, employment, and access to basic services.

Furthermore, the results demonstrate that the responses are sensitive to the specific wording of the subjective welfare question, where similar but different wording of scales (“satisfied” versus “enough”) can result in different estimates of the impact of a crisis. And lastly, the results are also sensitive to who in the household responds to the subjective questions on welfare. Overall, the results demonstrate the importance of standardizing scales in questions across households, or to focus on questions where the scales are identical across households.

The results are particularly relevant for the current high-frequency surveys being used to track welfare through the COVID-19 pandemic (e.g., World Bank 2020), and will also likely become more relevant in other settings as the need for high-frequency monitoring in conflict and fragile environments continues to increase (e.g., World Bank 2017b). Subjective metrics are often easier to collect than entire consumption modules; and including subjective welfare metrics that are official indicators tracking progress in achieving the Sustainable Development Goals in the high-frequency surveys can allow a comparison to the last household survey and to international benchmarks. Because of these advantages, many of the high-frequency surveys currently being conducted to track the COVID-19 pandemic include questions in which the scale is not explicitly introduced, and it might be difficult to interpret these changes to these questions as the pandemic evolves.

However, there are a number of questions that this analysis is unable to address. First, these are all short-term effects of the capture. The survey ended three months after the capture of the city, and it is unclear how long the impacts might last. It is possible that satisfaction levels only improved in the short run in Sana’a immediately 19
Energy consumed by the transportation sector was obtained from International Energy Agency (IEA) publications (IEA, 2014c, 2014d). To compare the various sources, the amount of energy consumed was converted to the same energy unit, terajoules (TJ).

Prices of various fuels were obtained from the IEA (IEA, 2014e). The data included the consumer prices of the various fuels for the residential and industrial sectors. In the analysis, we used gasoline and diesel prices at the residential level and LPG, natural gas, and electricity prices at the industrial level. Although the prices of the residential and industrial levels are highly correlated, differences do exist. In the analysis, we ran the empirical models with prices both before and after tax and compared the results. All price data collected were converted to constant 2010 US dollars using the consumer price index. Since the fuel price data are available only for OECD countries with some exceptions, we had to limit our analysis for OECD countries only. The annual price data are 12-month averages of what end-users pay. These prices include transportation costs and reflect the actual price paid by end-users; the prices are net of rebates (IEA, 2014e).

Considerable variations exist across countries when levying domestic taxes. Total tax revenues as a share of GDP among OECD countries in 2014 fluctuated between 19.8% and almost 50.9% (IEA, 2014e). Although in many European countries tax revenues exceed 40% of GDP, European countries generally provide much more extensive government support to their citizens. When comparing among OECD countries, the United States relies the least on taxes on goods and services, which averaged 17% of total 2013 tax revenues, whereas the average among OECD countries is 32.7% and Mexico collected about 49.8% of its tax revenues from consumption taxes and Chile collected 54%.<sup>5</sup> Fuel taxes are no different. The after-rebate price of fuel to end-users

> 5 Data available at https://stats.oecd.org/Index.aspx?DataSetCode=REV (viewed March 28, 2016).

11
_OPEN,_ the ratio of the sum of imports and exports of goods<sup>andservices in</sup> percent of GDP (referred to in what follows as the "trade<sup>opennessindicator"),</sup> which aims to capture exposure to extemal shocks; _TARIFF,_ the average tariff rate (that is, total tariff revenue<sup>dividedbythe</sup> value of imports).

The most common approach to examine the impact of financial<sup>opennessin</sup> cross-country studies is to build an index of capital account<sup>restrictionsonthe</sup> basis of the qualitative information reported in the<sup>IMF's</sup><sup>_AnnualReporton_</sup> _Exchange Arrangements and Restrictions._<sup>_15_</sup> The trouble<sup>withthis approachis that</sup> it provides no clue regarding the _intensity_ of capital restrictions--or,<sup>inversely,the</sup> effective degree of capital account liberalization. Here, I<sup>choseadifferentroute</sup> and opted for an "effective" measure of financial globalization,<sup>the ratioof foreign</sup> direct investment flows (FDI) to GDP. 16 If trade openness lowers poverty, the first variable should have a<sup>negative</sup> coefficient, whereas the second should have a positive<sup>coefficient.Similarly,if</sup> financial integration reduces poverty, the FDI-to-GDP ratio<sup>shouldhave anegative</sup> coefficient in the estimated regression.

It is important to acknowledge at the outset that all three<sup>oftheabove</sup> measures of globalization are problematic, because they<sup>captureonlyindirectly</sup> the process of trade and financial openness. For instance,<sup>thetradeopenness</sup>

> 15For instance, in their study on capital flows to transition economies, Garibaldi et al. (2002) constructed indices of restrictions on foreign direct investment<sup>andportfolio investment.The</sup> categories covered in the first index are approval requirements,<sup>the extentto whichprofitscanbe</sup> remitted abroad, ease in liquidating assets, and whether or<sup>notdirect investmentbenefitsfrom</sup> preferential treatment.

> 16A complementary measure of financial globalization,<sup>inlightofthegrowinginter-</sup> nationalization of banking, would be the share of assets<sup>of thedomestic financial systemheldby</sup> foreign banks. However, using the Bankscope database,<sup>Iwasunabletoobtainsufficient</sup> observations to include It in the regressions. Yet another<sup>approach would be to use the indicator of</sup> intensity of capital controls developed by Edison and<sup>Wamock(2001); however,their calculations</sup> pertain mostly to middle-income countries.

29
**Table 4:** Percentage of Job Flows in a Certain Size Class, Total Economy, 1990s

||**Gros**|**s Job **|**Realloc**|**ation**||**Job Cr**|**eation**||**J**|**ob Des**|**tructio**|**n**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|_<_20|20-49|50-99|100+|_<_20|20-49|50-99|100+|_<_20|20-49|50-99|100+|
|Germany|0.467|0.140|0.093|0.300|0.440|0.149|0.102|0.309|0.51|0.129|0.082|0.278|
|Finland|0.394|0.103|0.067|0.436|0.419|0.088|0.055|0.438|0.369|0.12|0.080|0.431|
|France|0.173|0.133|0.110|0.584|0.130|0.085|0.103|0.682|0.220|0.185|0.119|0.477|
|Italy|0.522|0.130|0.073|0.276|0.492|0.142|0.085|0.280|0.568|0.116|0.059|0.256|
|Portugal|0.457|0.153|0.097|0.292|0.471|0.152|0.094|0.283|0.449|0.152|0.099|0.300|
|United States|0.315|0.131|0.087|0.467|0.279|0.132|0.089|0.499|0.361|0.130|0.085|0.423|
|Argentina|0.397|0.154|0.106|0.342|0.367|0.158|0.112|0.362|0.433|0.147|0.097|0.322|
|Mexico|0.377|0.137|0.099|0.386|0.319|0.137|0.103|0.442|0.462|0.138|0.094|0.307|
|Estonia (1990s)|0.365|0.172|0.125|0.337|0.414|0.167|0.114|0.306|0.318|0.180|0.139|0.363|
|Hungary (1990s)|0.273|0.134|0.118|0.475|0.296|0.144|0.107|0.453|0.251|0.125|0.127|0.497|
|Latvia (1990s)|0.383|0.141|0.104|0.371|0.390|0.137|0.101|0.372|0.376|0.150|0.112|0.363|
|Slovenia (1990s)|0.227|0.088|0.100|0.585|0.293|0.100|0.090|0.517|0.169|0.076|0.112|0.643|
|Estonia (late 1990s)|0.365|0.172|0.125|0.337|0.414|0.167|0.114|0.306|0.318|0.180|0.139|0.363|
|Hungary (late 1990s)|0.317|0.142|0.108|0.433|0.337|0.149|0.106|0.408|0.294|0.132|0.111|0.463|
|Latvia (late 1990s)|0.421|0.143|0.107|0.328|0.437|0.139|0.107|0.317|0.398|0.150|0.109|0.343|
|Slovenia (late 1990s)|0.287|0.104|0.099|0.510|0.328|0.121|0.084|0.467|0.244|0.085|0.116|0.555|
|We do not observe fr|ms with|sales b|elow a g|iven thr|eshold|in Franc|e.||||||

Source: Own calculations based on harmonized firm-level database.