rafmacalaba/data-use-mentions-extended huggingface dataset:
1.53)|(2.13)|
|Log of exporter's MaxTime-AvgTime|-0.273|-0.235|-0.236|
|difference|(-5.07)|(-4.25)|(-4.28)|
|Log of importer's MaxTime-AvgTime|0.189|0.120|0.090|
|difference|(2.45)|(1.57)|(1.18)|
|Log of importer's total cost|-0.569|-0.518|-0.492|
|of import-related procedures|(-12.14)|(-11.0)|(-10.68)|
|Exporter's Corruption Perception Index||0.165|0.188|
|||(7.2)|(10.82)|
|Importer's Corruption Perception Index||0.183|0.134|
|||(7.22)|(6.27)|
|Regional trade agreement|0.270|0.332|0.343|
|dummy variable|(3.71)|(4.58)|(4.73)|
|_K_|12|14|12|
|_R_-squared|0.712|0.717|0.717|
|Adjusted_R_-squared|0.711|0.717|0.716|
|Observations|5149|5149|5149|
|_F_-statistic|1305.7|1101.0|1287.0|

Note: Dependent variable is total bilateral exports (in logs) in 2003 or latest year available. Corruption Perception Index is for 2004, from Transparency International. Figures in parentheses are _t_ -statistics (based on robust standard errors). Constant terms are not shown. Source: Authors' calculations.

18
of Urban Economics_ . doi: 10.1016/j.jue.2011.06.001.

- Mehlum, Halvor, Karl Moene, and Ragnar Torvik. 2006. "Institutions and the

- Resource Curse*." _Economic Journal_ 116 (508):1-20. doi: 10.1111/j.14680297.2006.01045.x.

- Michaels, Guy. 2011. "The long term consequences of resource-based

- specialisation." _The economic journal : the journal of the Royal Economic Society_ 121 (551):31-57. doi: 10.1111/j.1468-0297.2010.02402.x.

- Ministry of Finance, MOF. 2014. Final Report on the Production of Oil & Gas Sector GHEITI Report For 2012 and 2013. Accra, Ghana.

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- Sachs, Jeffrey D. 1995. _Natural resource abundance and economic growth_ . Edited by Andrew Mark Warner and Development Harvard Institute For International. Cambridge, Mass.: Cambridge, Mass: Harvard Inst. for Internat. Development.

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- Van der Ploeg, Frederick. 2011b. "Natural resources: curse or blessing?" _Journal of Economic Literature_ 49 (2):366-420.

21
higher than the second highest price found in Uganda. At the opposite end of the spectrum, the lowest prices were found in República Bolivariana de Venezuela and Egypt, closely followed by the Islamic Republic of Iran and Iraq for gasoline and diesel, Indonesia and India for kerosene, and Iraq and Bolivia for LPG. República Bolivariana de Venezuela stands out as having petroleum product prices far lower than in any other country, with the exception of LPG, for which the official price in Egypt was even lower.

Table 2: Summary statistics on retail prices in July 2012

|**Statistic**|**Gasoline**|**Diesel**|**Kerosene**|**LPG**|
|---|---|---|---|---|
|Minimum price, US$/liter or US$/kg|0.02|0.01|0.18|0.04|
|Maximum price, US$/liter or US$/kg|2.20|1.97|1.60|4.74|
|Average price, US$/liter or US$/kg|1.13|1.01|0.86|1.17|
|Median price, US$/liter or US$/kg|1.17|1.05|0.93|0.99|
|Total number of countries|64|64|44|52|

_Sources_ : See Tables A2.1 in Kojima (2012) and sources under “information” in Table A1.1. _Note_ : Prices are in US$/liter for gasoline, diesel, and kerosene and US$/ kg for LPG. Prices available in Jamaica are tax-inclusive ex-refinery prices, and distribution margins can be significant. They are used for pass-through calculations but not for computing statistics on retail prices.

The prices in July 2012 were compared to those in June 2009 to
The results of the targeting simulation are relevant to cases where targeting is based solely on the ranking of municipalities – all methods appear to do a solid job. Nevertheless, there are many instances where the targeting mechanism may rely on the method’s poverty estimates. For example, Senegal’s Unique National Registry (RNU - in French) of vulnerable households relies on actual estimates from poverty maps to determine the quotas for sampling by municipality (Ferre 2018). It is in those cases where a method that yields unbiased and precise estimates should be employed.

# **6 Conclusion**

This paper sets out to validate traditional small area estimation and machine learning approaches for poverty mapping. Machine learning approaches have become popular across the globe for producing highly disaggregated estimates of poverty which often feed into policy making. The methods are particularly popular in the most data deprived scenarios where census data are not frequently collected, and even when collected may not be contemporaneous to a household survey. Hence, the methods are also a popular alternative for cases where the census data is too old for use under a traditional unit-level small area estimation exercise. To the best of our knowledge, a rigorous validation of machine learning methods like the one done here, where machine learning methods are compared to traditional small area estimates and to a true welfare measure, has not been done before.

We first showed analytically that that the _R_<sup>2</sup> can be a misleading measure of model performance: it is biased downward when direct estimates are used as the reference measure of model performance, it is insensitive to differences in location and scale between the reference measure and the model predictions, and it is influenced by the variance of the reference measure. One practical issue this raises is that it can be misleading to compare the performance of poverty mapping methods across different datasets, as is done in Yeh et al. (2020), Chi et al. (2022), and Aiken et al. (2022), among
The petty cash and project advance data for 2018 is shown in the attached figure. The total amounts are a relatively modest percentage of the total disbursements. The petty cash process, when limited to small amounts on an overall basis and a low individual transaction threshold, is generally a good way for developing countries to provide easy access to small amounts of money to SUs like schools and health clinics and has a positive impact on service delivery.

_Figure 5 Advances as Percent of Total Disbursements_ <!-- Start of picture text --> 16.0<br>13.9<br> 14.0 12.7<br> 12.0 10.9<br> 10.0<br> 8.0<br> 6.0<br>3.3<br> 4.0 2.8 2.5<br> 2.0<br> ‐<br>%of total %of total %of total<br>Total Center Provinces<br> Petty Cash Project Advance<br>Percentage<br><!-- End of picture text -->

# _Source:_ Authors.

An analysis of the frequency distribution of project and program advances and of petty cash advances was therefore done (see Annex II for details). From it, the following is evident:

- 83% of the advances are below 100M Riels or US$25,000;

- 25% of the Petty Cash advances at Center are above 400M riel or US$100,000; and

- - At the provinces 95% are below 100M Riel or US$25,000.

Some petty cash transactions are quite large. Out of a total of 4,098 petty cash transactions in the first 4 months of 2018, only 100 transactions which are more than 400,000,000 Riel would constitute almost half of the total amount 199 billion i.e. 98 billion Riel. Out of these 10 transactions 27 take place at the
average estimate. In addition, our estimates are robust to different model specifications.

Using this elasticity, we construct a new measure of economic activity that is based on the optimal linear combination of light-predicted GDP growth and GDP growth as reported in national accounts. The new measure has optimal weights of 67 percent on light-predicted GDP growth and 33 percent on official GDP growth. This new measure can be especially informative during periods in which official data are less likely to capture economic activity properly, such as during the COVID-19 pandemic.<sup>4</sup> As expected, higher levels of development and statistical capacity reduce both overand underestimation of GDP. A higher score on voice and accountability, one of the World Bank’s Worldwide Governance Indicators, reduces overestimation, in line with a crucial cross-checking function of stakeholders. During COVID-19, the new measure suggests that official statistics may have overstated the negative economic impact for countries with large manufacturing and service sectors. Finally, we employ the spatial granularity of the data and the estimated elasticity to understand subnational heterogeneity of the COVID-19 impact. We find that economic activity declined more in richer districts, likely because services and industrial activity suffered more than agricultural activity. Areas with a higher population density were also hit harder, in line with containment measures being more disruptive for them.

The rest of the paper is organized as follows. Section 2 discusses related literature. Sections 3 and 4 describe the VIIRS nighttime light data and important stylized facts, respectively. In Section 5, we present our approach to estimate the elasticity between nighttime light intensity and GDP growth. Section 6 constructs a new measure of economic activity using nighttime lights and compares it with national accounts data. Section 7 analyzes the impact of COVID-19 at the subnational level. Section 8 concludes.

# **2 Related Literature**

Nighttime light data from the Defense Meteorological Satellite Program (DMSP), available at annual frequency from 1992 to 2013, have been used extensively in a wide array of economic studies. Among others, the
, the results produced hinge on the quality of the asset data to the same degree. The geolocated datasets of hotels and schools are sourced from volunteer contributions, and do not represent all hotels and schools in Vietnam (sections 3.6 and 3.5). It is possible that the nature of crowd-contribution leads to biases in the representation of subgroups of each asset class. For example, as crowdsourcing is unpaid and requires an internet connection, contributions made could overrepresent more easily accessible areas, and lead to the underrepresentation of installations from poorer or otherwise less accessible villages. This is especially true for the results for schools, where the provincial distribution does not match the distribution from the General Statistics Office of Vietnam, and should be interpreted with great care. Still, in data-sparse environments, openly available data can provide second-best options for approximating hazard exposure. Despite their imperfection, such estimates can be valuable in disproving the absence of risk which may be suggested by the absence of information. Results relying on crowd-sourced data thus do not represent hard facts, but best estimates forming the basis for discussion.

In other sectors, asset and socioeconomic data is more reliable, and this leads to greater certainty in the results regarding their exposure. In particular, the analysis uses datasets leveraging new developments in the processing of remote sensing data. These datasets provide highly granular estimates of the locations of specific assets like aquaculture ponds, data which would be prohibitively expensive to collect by other means. Due to the cheapness of computing power and the ease of automated spatial analysis, information about exposure can be created at higher resolutions than ever before. As methodologies to extract specific asset locations from various types of remote sensing data become more established and the barriers to implement these algorithms become lower, the translation of such methodologies to different settings can be expected to produce insights into distributions of natural hazard risk in new regions.

Overall, this paper shows that the exposure of Vietnam’s people and their economy is significant, but that this exposure varies greatly.
dismissing employees (World Bank 2020b). In 2020, Senegal scored a 0 in terms of the Ease of Hiring Index and 60.0 for the Ease of Redundancy Index.<sup>6</sup> This is further corroborated by Golub et al. (2015),<sup>7</sup> which rank Senegal as the third most highly regulated labor market in the world, and the World Economic Forum’s labor market competitive index, where Senegal ranks among the 30 least competitive countries in terms of its labor market.<sup>8</sup> A closer look at the data shows that countries with high levels of informality are associated with lower levels of competitive labor markets (Figure 2, Panel A). This is particularly the case for SSA, which has the highest levels of informality and lowest levels of labor market competitiveness in the world. The opposite is true for countries in Europe and Central Asia, which have a low labor informality rate and are associated with more effective labor markets.

Quality jobs and formality yield higher levels of productivity. This rapport is well documented in the literature (World Bank 2020c; Brummund, et al. 2016; La Porta and Shleifer 2016; Busso et al. 2012; Benjamin et al. 2012; Hsieh and Klenow 2009, Steel and Snodgrass 2008; Gelb et al. 2009; La Porta and Shleifer 2008). Countries with high informality tend to have the lowest levels of worker productivity in the world, as measured by their output per worker (Figure 2, Panel B). This sheds light on the substantial productivity gap between formal and informal firms (World Bank 2019). In Senegal, La Porta and Shleifer (2014) find that the value added per employee of informal firms is 14 percent that of formal firms. Therefore, expanding the share of workforce in formal employment is a critical policy objective for developing countries in order to increase productivity.

> 6 The lower the score, the least flexible regulation
changes over time. Based on the WRSI for individual crops, LEAP permits the calculation of a basket WRSI reflecting the WRSI for a basket of crops grown in a given area.

- (iii) **_Calculation of yield reduction:_** As an intermediary output, the LEAP tool computes yield reduction estimates. A 0% yield reduction would reflect that the harvest would be at its potential, and a yield reduction of 100% would reflect a complete crop failure. Yield reduction is computed for each crop and each region, and is dependent on actual evapotranspiration, total water requirement without water stress and a yield response factor representing the effect of a reduction in evapotranspiration on yield losses<sup>43</sup> . Based on yield reduction estimates for individual crops, LEAP permits the calculation of yield reduction of a basket of crops grown in a given area.

- (iv) **_Calculation of estimated beneficiary numbers:_** Based on previously computed WRSI data in combination with past beneficiary numbers and demographic data, the LEAP tool computes estimated beneficiary numbers. In particular, the LEAP tool uses a logarithmic model to compute needs based on the following parameters:

- a. **_Needs in case of optimal rainfall_ :** Number of people in need of assistance in case of optimal rainfall in any given region. In particular, the level of needs in case of optimal rainfall may be interpreted as the number of chronically food insecure people, as their status of food insecurity is independent of crop performance.

- b. **_Population at risk of food insecurity_ :** This number corresponds to the maximum number of beneficiaries in the case of the most severe drought possible, and should include the entire population at risk of food insecurity due to drought in a given area. To optimally estimate this parameter, this variable should be based on field assessments to assess the number of people faced with drought-related food insecurity. However, in the absence of recently updated data, LEAP selects the maximum
With these measures, we construct a vector of urban governance indicators which we use to test the impact of urban governance on city performance. The vector consists of measures of voice and participation and transparency and accountability which we take from the UN database. We also use measures like illegal financing, state capture, bribery in utility, and bribery in judiciary from the Kaufmann, Léautier, and Mastruzzi (KLM) database. Finally, we construct a dynamic indicator of city transparency which uses proxies like, whether a city has a web page, what information is included in the web page, whether the city budget is publicly available on the web or whether companies can register on the web. Note that this indicator captures similar aspects as the one described earlier in Section 2 and attributed to Townsend (2001).

# **3.3 Defining the Impact of Urban Governance on Globalized Cities**

A ‘globalized city’ as an _enduring performance_ is a concept worth testing empirically. We can ask “what aspects of governance give cities this capability to constantly translate the global complexities into an advantage to them which can be seen in the way they treat their citizens (access to services, quality of services); attract firms to invest (public-private partnerships, FDI, firm location); or measures such as maintaining economic growth over several years and increasing the average per capita income of a city’s citizens. Doel and Hubbard (2002) suggest that a globalized city is always a work in progress being shaped and managed by firms, states, sectors, and cities. Thus a city which has good governance should be able to perform well and remain in the league of important global cities as well as deliver value to its citizens.

The literature on globalization and urban governance has two schools of thought on the importance of globalization for the performance of cities. There are those who argue that cities can be well performing if they have sufficient spatial agglomeration of know-how and capacity in the city and that a city’s international
This paper introduces a new cross-country data set collected through a survey of regulators at the country level, and expands on the existing literature to analyze access to finance by SMEs. The paper draws on the report and database of _Financial Access 2010_ (CGAP and WBG, 2010), the second in a series of annual reports by CGAP and the World Bank Group (WBG) on the financial inclusion agenda based on survey data collected from financial regulators around the world. More specifically, this new data set collects information on the level of lending to SMEs, the definition of SMEs, and country-level initiatives through which lending to SMEs is monitored. It is also the first attempt at collecting global comparable SME statistics.

This new database collects data from financial regulators, mostly central banks and bank superintendents, around the world. Although regulators collect a wide array of portfolio data from regulated financial institutions, SME lending data are not necessarily among the regularly collected statistics, and some parts of SME lending are done through unregulated institutions. Nevertheless, according to _Financial Access 2010_ , the experience of some countries shows that collection of data on lending to SMEs is possible on a monthly basis with a clear SME definition and sound reporting requirements.

The analysis of the new data indicates two main findings. First, many regulators collect data on SME financing; however, there is no unique definition of an SME. At the same time, the results indicate that differences in definitions across countries are not statistically significant in explaining the differences in SME lending volumes. Second, given this, the paper goes on to estimate the global SME lending volume at $10 trillion, roughly two-thirds of the current size of the US economy. The bulk of this volume, 70 percent, is in high-income OECD countries. On average, SME loans constitute 13 percent of GDP in developed countries and 3 percent in developing countries.

The rest of the paper is organized as follows. Section 2 introduces the dataset. Econometric analyses are reported in section 3, and predictions of global
Figure 3.5: _Employment outcomes are better in countries where government does not interfere into business activities._ <!-- Start of picture text --> 5 est<br>lva<br>cze kaz<br>hun bgr<br>svk<br>ltu<br>ukr<br>0 pol<br>hrv rom<br>geo<br>svn<br>mda<br>-5<br>2 4 6 8<br>Government interference index<br>sv emp. controlled for gdppc<br><!-- End of picture text --> Figure 3.6: _The quality of the legal system is one very important aspect of government interference. More efficient and independent judicial system is good for employment._ <!-- Start of picture text --> 4 lva<br>cze<br>2 bgr hun<br>ltu svk<br>ukr<br>0 pol<br>rom hrv<br>-2<br>svn<br>-4<br>20 30 40 50 60<br>Legal system<br>sv emp. controlled for gdppc<br><!-- End of picture text --> 34
on GFS is limited, we instead use gross debt from World Economic Outlook (WEO). All government variables are as a ratio to GDP.

This paper focuses on the impact of tax on two measures of economic output. First, we calculate 10and 15-year ahead growth as the log change in real GDP per capita from the World Bank’s World Development Indicators (WDI). To measure inclusive growth, we use the recently created “prosperity gap” by Kraay _et al._ (2023) and again look at the 10- and 15-year ahead level of the prosperity gap. The prosperity gap is defined as the average factor by which incomes need to be multiplied to bring everyone in a country to an income of $25 per day.

# **5. Results**

# **5.1 Identifying the threshold**

about 12.5 percent (figure 3). This threshold is determined by excluding resource-rich economies,<sup>3</sup> which often exhibit lower tax revenues<sup>4</sup> but higher and volatile growth outcomes. For the optimal threshold, we conduct a grid search, commencing from 7 percent (the 5th percentile of the tax-toGDP ratio) to 30 percent, with intervals of 0.1. The results are in line with those presented in GJW. Interestingly, the threshold has not moved up despite the significant growth in spending needs since the previous empirical work was conducted.

**Figure 3: The growth maximizing tax to GDP ratio hovers around 12.5 percent**

_(10-year ahead) (15-year ahead)_ <!-- Start of picture text --> 90% Confidence intervals Coefficient 90% Confidence intervals Coefficient<br>60 60<br>40 40<br>20<br>20<br>0<br>0<br>-20<br>-20<br>-40<br>-40 -60<br>-60 -