rafmacalaba/data-use-mentions-extended huggingface dataset:
Consistent with the literature examining promotions of local officials using prefecture-level data in China (Landry, Lü and Duan 2017), we fail to find any economically and statistically significant effect of reported performance on promotion likelihood for PSs. Alternatively, we adopt an ordered probit model, as in Li and Zhou (2005), to distinguish lateral moves from demotions. In column 4, we show the marginal effect of average reported GDP growth on the likelihood of promotion. Though the coefficient now is more precisely estimated, the magnitude of this effect, similar to that of the linear probability model, is small. Increasing reported growth by one percentage point leads to an increase in the probability of promotion by only 0.46 percentage point, amounting to 6% of the mean.<sup>46</sup> Since the reported performance does not contribute much to promotion for city secretaries, does true performance matter? This scenario represents the case of the provincial government being able to distinguish data faking and real performance. In columns 2 and 5, we fail to find any significant effect of true GDP growth predicted by the nighttime lights on the likelihood of promotion. This is consistent with the fact that the true performance is hidden from provincial leaders.

Finally, to test whether manipulation matters, we examine whether promotion is related to GDP over-reporting after controlling for the predicted true economic growth. In column 3 of Table 9, we find a statistically significant and large coefficient of over-reporting. A one percentage point increase in growth over-reporting leads to an increase of 1.7 percentage points in the likelihood of promotion, or a 22 percent increase of the mean promotion rate. Since over-reporting is a generated variable, we also estimate the standard error using the bootstrap method with 1,000 replications. The result, reported in brackets in column 3, is similar, and the coefficient of the over-reporting variable stays significant at the 10% level. In column 6, we use the ordered probit model. Again, the magnitude is similar to
sup>5</sup> 51,137 road shipment data are used. A total of 8,952 carriers transported cargos among 397 locations. Each route is georeferenced. Note that the database is developed at the stop-by-stop level. Even on the

> 5 To exclude outliers, the letter-value display analysis was used, which identifies extreme values in the tails of the distribution that fall outside of three times the interquartile range (IQR) for each variable.
at understanding the impact of mismeasurement in EO products in estimating policy relevant relationships between climate and development outcomes in low- and middle-income countries.

Understanding the differences in results that arise when a researcher chooses one EO product over another also has practical consequences, due to the importance of smallholder agriculture for rural livelihoods in low- and middle-income countries. Agriculture is a source of both income and employment, and as such understanding how weather affects agricultural productivity on smallholder farms is of policy interest. Thus, these results are important for informing policies to develop and advise on improved agricultural technologies that can mitigate the risks posed by climate variability and extreme weather events, and to provide social protection measures to smallholder farmers that are exposed to climate shocks.

# **2 Measuring the Truth: Gauge Station Data and Earth Observation Data in Africa**

The goal of weather data products is to measure and report on the objective fact that is the volume of precipitation and the temperature in degrees in a given location at a given time. In theory, this goal is easily accomplished using technology that has existed for centuries. One simply needs a rain gauge to collect precipitation, a mercury thermometer to measure temperature, and someone to record this data with pencil on paper. What could be simpler than measuring the weather? Humans have been reporting on the weather since the invention of writing and have been using scientific instruments to record precipitation and temperature since the 1600s (Lundstad et al., 4
Model2, ChinnIt, Fad Model2, ChinnIt, FemeIn Model2, Slope, Fg Model2, Slope, Fad Model2, Slope, FemeIn<br>15 1<br>10 0<br>5<br>0 -1<br>-5 -2<br>0 5 10 0 5 10 0 5 10 0 5 10 0 5 10 0 5 10<br>Years Years<br>95% CI Cumulative dynamic multipliers 95% CI Cumulative dynamic multipliers<br><!-- End of picture text --> Figure A.3: Cumulative effect of a permanent one-standard deviation increase in the average over advanced and emerging countries of the ChinnIto capital account openness measure (average of the contemporary value and its 1 and 2 years lags, left panel) and the slope of the yield curve (10year US treasury constant maturity minus federal funds rate, right panel), over the estimated factors, based on the regression model of columns 1-3 of table 8 (top graphs) and the expanded regression model in columns 4-6 of table 8 (lower graphs).

47
which nevertheless tends to be of a limited scope at the sub-national level, but also, and more importantly, to avoid disruption in the financing of public investment in bad times.<sup>19</sup> In this regard, evidence provided by Sorensen _et al._ (2001) suggests that the states that have relatively tight balanced-budget rules seem to have less pronounced swings in both revenue and expenditure over the business cycle than the states with less stringent fiscal rules.

# **_4.2 Overview of Brazilian municipal finances: a focus on infrastructure investment_**<sup>**_20_**</sup>

19. There are several constraints to the ability of Brazilian local governments to boost spending on infrastructure investment. They range from institutional provisions on the assignment of expenditure functions across and within the different levels of government to a lack of budget flexibility, against a backdrop of increasing downward rigidities in current spending and on-going fiscal adjustment.

20. On institutional constraints, the Brazilian Constitution empowers the local governments to delegate to the private sector, often through concessions, the provision of local services such as urban transportation, and water and sanitation. These services, together with housing and urbanization, are provided primarily by the municipalities (Table 2). Other services requiring considerable infrastructure investment, such as inter-municipal transport and gas distribution, are under the purview of the states, whereas energy, telecommunications and inter-state transportation, among others, are assigned to the federal government.<sup>21</sup>

- 19 . This is consistent with the evidence reported by Bohn and Inman (1996), which, although sensitive to the cyclical indicator used to gauge fiscal responsiveness, indicates that stringent fiscal rules encourage precautionary savings in good times, which can be used subsequently to finance counter-cyclical measures in bad times. By contrast, also using U.S. state data, Alesina and Bayoumi (1996) argue that fiscal rules have indeed reduced flexibility in state-level fiscal policymaking without, however, having a
assistance under the 2019 framework, families had to be registered in the single registry Cadastro Unico<sup> ́</sup> (CadUnico).<sup>13</sup> BF consisted of two benefits: (i) the basic benefit and (ii) the variable benefit. As of 2019, the final year of our panel dataset, the monthly basic benefit was paid to families with a monthly per capita income of less than R$ 89 (USD 21.4). Families whose per capita income remained below R$ 89 (USD 21.4) also received a supplement so that the household would reach at least a monthly per capita income of R$ 89 (USD 21.4). The variable benefit was granted to individuals with a monthly per capita income below the

> additional hours of informal work).

> 12Other references are Aguilar-Gomez _et al._ (2019) for Mexico, Berniell _et al._ (2021) for Chile, and Heath (2017) for theoretical evidence from Ghana.

> 13Individuals with a per capita income below 0.5 of the minimum wage (MW) were eligible to register in CadUnico. For more information about the registry, see Section 2.2.

5
br>(0.070)||||
|IPV often index|||||
|treatment x post|0.019<br>(0.094)|-0.049<br>(0.087)|0.000|2,314|
|treatment x post x Goal|-0.090<br>(0.139)||||
|IPV in last year index|||||
|treatment x post|0.118<br>(0.146)|-0.020<br>(0.153)|0.000|2,314|
|treatment x post x Goal|0.058<br>(0.222)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 2. For each outcome defined, the coefficients from a single regression are presented in two sub-rows, with estimates of _β_ 1 in column 1 and _β_ 2 in column 2, labeled treatment x post, and the estimate of _γ_ 1 in the second of the two rows, labeled treatment x post x Goal. Column 3 presents the outcome mean at endline in among males whose connected female is in a ELA only community and was not assigned to the _Goal_ treatment and column 4 shows the number of observations in the model. All specifications include controls for age of the male, highest grade completed, a binary indicator that the male never talking to his father about sexual reproductive health topics, a binary indicator that the male’s household owns the house he lives in, and region fixed effects. Tolerate violence from husband/partner is a binary indicator that male agrees that a female should tolerate violence, Men can beat women under certain circumstances is a binary indicator for the male disagreeing that “a man should not beat a woman under any circumstance,” and the Violence Attitudes index is generated by taking the unweighted mean
of the sectors in the considered country (OWN), we can measure the extent of international sourcing from each of the 8 country groups.<sup>15</sup> 2. Export share towards intermediate input use: by dividing the value of production that is used as intermediate inputs in each of the 8 destination regions—shown in different columns of the IO table—with the total output of the sector, we can measure the extent of integration into GVCs as a supplier.

3. Export share towards foreign final demand: we similarly divide the export flows consumed as final demand in the eight regions with the sectoral output to measure the general outward orientation of different countries.

4. Domestic value added in gross exports: we use the extraction method proposed in Los, Timmer and de Vries (2016) to calculate the value added to export ratio (VAX) that was introduced by Johnson and Noguera (2012). We perform these calculations for the entire economy as well as for the manufacturing sector alone.

# **3.3 Patterns in international sourcing**

The first thing we document is to what extent each country sources intermediate inputs from abroad. Table A.2 in the Appendix list the shares for all countries and origins for the manufacturing industry in 2015. On average (using population weights to aggregate countries), local value added within the industry is 41% and 59% of the output value consists of purchased intermediates. By far the most important source of purchased inputs are the other industries within in the same country. On average they account for 45% of total output value or approximately three quarters of all purchased inputs. Together, local value added and domestically sourced intermediates account for 85% of the total value of the manufacturing industries in sub-Saharan Africa and only the remaining 15% is imported.

An important pattern to point out is that the results for some countries seem highly unbelievable. For several countries, the local value added share is implausibly low, e.g.
on Mobile Banking in Bangladesh.” _American Economic Journal: Applied Economics_ , 13(1): 38-71.

Munyegera, Ggombe Kasim and Tomoya Matsumoto. 2016. “Mobile Money, Remittances, and Households Welfare: Panel Evidence from Rural Uganda.” _World Development_ , 79: 127-137.

Riley, Emma. 2018. “Mobile Money and Risk Sharing Against Village Shocks.” _Journal of Development Economics_ , 135: 43-58.

Sekabira, Haruna, and Matin Qaim. 2017. “Mobile Money, Agricultural Marketing, and Off-Farm Income in Uganda.” _Agricultural Economics_ , 48: 597611.

Suri, Taveneet, Jenny Aker, Catia Batista, Michael Callen, Tarek Ghani, William Jack, Leora Klapper, Emma Riley, Simone Schaner, and Sandip Sukhtankar. 2021. “Mobile Money.” VoxDevLit, 2(1), February 2021.

Suri, Taveneet, and William Jack. 2016. “The Long-Run Poverty and Gender Impacts of Mobile Money.” _Science_ , 354(6317): 1288-1292.

Unnikrishnan, S., J. Larson, B. Pinpradab, and R. Brown. 2019. “How Mobile Money Agents Can Expand Financial Inclusion.” Boston: Boston Consulting Group, 14 February. https://www.bcg.com/en-gb/publications/2019/how-mobile- <u>money-agents-can-expand-financial-inclusion.aspx</u> USAID and Citi Foundation. 2012. “10 Ways to Accelerate Mobile Money: USAID-Citi Mobile Money Accelerator Alliance.” https://www.findevgateway.org/paper/2012/01/10-ways-accelerate-mobilemoney-usaid-citi-mobile-money-accelerator-alliance Yang, Dean, and HwaJung Choi. 2007. “Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines.” _World Bank Economic Review_ , 21(2): 219-248.

Young, Nathaniel. 2015. “Formal Banking and Economic Growth: Evidence from a Regression Discontinuity Analysis in India.” Mimeograph, Boston University.

20
identification documents, have low experience in urban jobs that have high local demand, and tend to have lower education relative to the rest of the Colombian population (Ib ́a ̃nez and Moya, 2006), it is plausible that upon arrival at urban centers, IDP take lower tier jobs, which are most likely to be available in the informal sector (Amaral and Quintin, 2006; Perry, 2007; Galiani and Weinschelbaum, 2012; La Porta and Shleifer, 2014a; Meghir et al., 2015), increasing its size.

A larger informal sector may end up hurting formal firms’ performance in several ways. Formal firms, for example, face higher costs relative to informal firms as they pay taxes, fees, and higher wages for their employees (La Porta and Shleifer, 2014a). Informal firms, thus, can usually carry lower prices and costs competing in an unfair way with formal businesses. Beyond reducing the demand for formal firms’ products via lower prices, unfair competition from informal firms could slow down the process in which inefficient firms can be replaced by more efficient competitors and negatively affect the incentives of formal firms to innovate and adopt new technologies (Perry, 2007). Competition with informal firms, consequently, may lead to productivity losses for the formal firms. In addition, since informal firms are able to use public goods but do not pay for them, this may lower the quality public goods and services and crowd out their use by formal firms (Besley and Persson, 2013). At the same time, higher informal competition may force formal sector firms to lower the quality of their products (as proposed by Banerji and Jain, 2007).

If our argument is correct, then we should observe that those formal firms operating in sectors that are more prone to informality competition should be disproportionately affected by IDP inflows relative to the other firms. To test this hypothesis we use the manufacturing survey firm data and split the sample according to industry codes in two groups of firms according
Table 2 lists the major importers of raw sugar for the period 1990-93. Former USSR, until 1991, and Russia, since 1992, are the largest importers of raw sugar. USA, UK and Japan and Korea are other large importers. On average, each of them imported over 1 mln MT per annum during 1990-93. In the 1960s and 1970s the USA used to be the largest importer, importing over 3 mln MT, about a quarter of world imports.

Table 2: **Principal importers of raw sugar, 1990-93**

|Amount|Countries|
|---|---|
|More than 1.7 mln MT|Former USSR / Russia|
|1.1-2 mln MT|USA, Japan, Korea, UK|
|0.5-1 mln MT|Algeria, Canada, Malaysia, China|
|0.3-0.45 mln MT|France, Sri Lanka, Morocco, Portugal|

Source: See Data S6 for details Data S6 shows the principal importing countries and their imports for the years 1965, 1975, 1985, and 1990-93. The countries listed accounted for over 95% of the total world imports during 1990-92. The developed countries accounted for 67% of total raw sugar imports.

The major exporters of refined sugar (including plantation white sugar) are given in Table 3. In the 1990s the EU was by far the largest exporter, on its own accounting for about 38% of the world's exports. In 1992 and 1993 there were large increases in exports of refined sugar from Brazil and China, which caused total world exports to jump about 50 percent above the levels prevailing during most of the 1980s. In the 1960s, in addition to being the largest exporter of raw sugar, Cuba was also the largest exporter of refined sugar, accounting for about 15 percent of world exports. By 1975 France had taken the lead, and Cuba's share dropped to less than 2% by the early 1990s. Data S7 shows exports by country
Also, a database<br>only showing subsidies will not be<br>sufficient. To develop this category the<br>Index drew on work fromOpen<br>Spending.|Open spending data shows<br>whether public money is<br>efficiently and effectively<br>used. It helps to understand<br>spending patterns and to<br>display corruption, misuse,<br>and waste.|Following data must be online to qualify for<br>assessment:<br>•<br>Government office which had the<br>transaction<br>•<br>Date of transaction<br>•<br>Name of vendor<br>•<br>Nominal amount of individual<br>transactionLevel of granularity<br>•<br>Individual record of each<br>transaction|
|Procurement|All tenders and awards of the<br>national/federal government<br>aggregated by an office. It does not look<br>into procurement planning or other<br>procurement phases such as<br>implementation (i.e. actual money<br>transfers, which are part of our<br>spending category). To develop this<br>category the Index drew on work from<br>theOpen Contracting Partnership.|Open procurement data may<br>enable fairer competition<br>among companies, allow to<br>detect fraud, as well as<br>deliver better services for<br>governments and citizens.<br>Monitoring tenders helps<br>new groups to participate in<br>tenders and to increase<br>government compliance.|Following data must be online to qualify for<br>assessment:<br>**Tender phase**<br>•<br>Tenders per government office<br>•<br>Tender name<br>•<br>Tender description<br>•<
markup premium for large companies is 12.2 percent higher than that of small firms (figure 9). Both size and age effects are still present, even after controlling for productivity differences (see table A1.6 in annex 1).<sup>33</sup>

**Figure 8. Average markup differences relative to young firms, by age category**

<!-- Start of picture text --> 35<br>30<br>22.8<br>25<br>20 15.8<br>15<br>7.9<br>10 4.8 5.4<br>5 2.2<br>0<br>-5 -1.60<br>Mature (between 6 and 15 years old)<br>Old (older than 15)<br>Source: World Bank staff calculations based on SBS data<br>set.<br>% difference<br><!-- End of picture text --> _Note:_ Numbers reflect estimated coefficients (multiplied by 100) for age dummy categories—relative to young firms (1–5 years old), the omitted category—in a fixed-effect regression model controlling for firm covariates plus year, county, and NACE 4-digit sector fixed effects, as displayed in equation (11). All bars reflect statistically significant results. See table A1.5 in annex 1 for the complete set of results.

**Figure 9. Average markup differences relative to small firms, by size category**

<!-- Start of picture text --> 32.7<br>35<br>23<br>25 16.9<br>12.2<br>15<br>5 0.90<br>-5<br>-15 -8.30<br>Medium (between 51 and 250)<br>Large (more than 250
some goods that are no longer relevant from a technological point of view. For example, the original definition includes items such as “audio tapes, cassette, full length”. We therefore restricted the focus to the subset of digital NAICS industries that used digital inputs in a meaningful way. Specifically, we use a table from the BEA Input-Output accounts data from 2012, which contains 405 x 405 industries (identifiable to 6-digit NAICS), to restrict further the original set of digital industries, defined as those that have at least 10% of digital inputs over total inputs. Table A1 in the appendix reports the set of digital goods we identified according to these criteria. We then used a concordance to match these industries to the HS classification used in trade statistics.

## **2.3 Other variables**

We further complement our data set with information on gravity-type variables, notably distance and language, which come from CEPII, as well as GDP and population, which we take from the Penn World Tables.

# **3 Key features of Africa’s imports of digital goods**

In this section, we document key stylized facts on Africa’s imports of digital goods over the period of analysis. In 2019, Africa imported $59.4 billion worth of digital goods. Figure 1 shows that digital goods constitute a smaller share of total imports of AfCFTA members (about 13 percent) compared to the rest of the world (around 21 percent). The figure also shows that between 2012 and 2019, the relative importance of digital goods in total imports remained relatively stable in AfCFTA, while it increased moderately in the rest of the world, from around 19 percent to 21 percent.

Table A.2 reports the main source markets for the imports of digital goods from AfCFTA members in 2019. We observe that the great majority of digital goods in Africa are sourced from higher-income countries and large emerging economies. Specifically, 35 percent were sourced from China, 27 percent from the European Union,
in 18 “distritos”. A “concelho” is an ensemble of persons resident in a certain geographical area. It is, also, a juridical entity with its own independent administrative bodies. Their average area is 323.4 square kilometers, approximately 10% of the US county (with a standard deviation of 283.3).

All the data was aggregated from the plant level, to focus on “concelhos” and two-digit ISIC manufacturing sectors. Due to the well known problems of estimating productivity in non-manufacturing (see Griliches, 1994) in this paper I analyze only manufacturing sectors. The choice of the plant, and not the firm as the level of analysis is not arbitrary. At the firm level what matters is the regional location of the head office of a firm and, therefore, all the employment in plants outside this region is reported to the region of the head office. The average costs of labor supported by the firm and the potential experience and years of schooling of the workforce were obtained aggregating the worker level data to the plant level using the plant identifier in each worker record.

Figure 3 illustrates the evolution of unemployment rate and the cyclical component of GDP for the Portuguese economy during this period. The period 1985-1994 goes from bottom to bottom of the business cycle, and is sufficiently long to capture long term growth without being too much affected by cyclical considerations. Also these years are nearly equivalent with respect to the unemployment rate. During this period, national employment rose 16.5% and the Portuguese employment structure also registers a reasonably fast change. Figure 4 shows that the share of industrial employment is falling while agricultural employment is almost unchanged. In industry employment increases slightly 1.8%, but total employment in manufacturing falls by 2.5%. Figure 5 decomposes the change in the employment structure for three-digit manufacturing sectors. Wearing apparel and footwear, both with a large tradition in the Portuguese economy, were the sectors registering larger growth. They went from 9.6% and