rafmacalaba/data-use-mentions-extended huggingface dataset:
(2011) and Ag ́enor (2014). Data sources used to match key ratios include the IMF’s World Economic Outlook (WEO) database, the oil and revenue management policy framework provided by the Uganda’s Ministry of Finance and various research papers such as Pritchett (2000), Goldberg (2011), Ang ́enor and Aizenman (1999), Kopoin et al. (2013) and Ag ́enor (2014).

In the representative household’s utility function, the weight on leisure _ψ_ is set to 3, which leads to the steady-state value of household work effort to be 40 percent of available time. The household’s discount factor, _β_ , is set 0.93, implying a long-run real interest rate of 9.68 percent annually. This assumption is fairly reasonable and consistent regarding banks’ interest rates in most of low-income countries (see African Development Indicators (ADI)). In addition, the share of capital in the production function for intermediate goods in the non-traded goods sector, _α_<sup>_N_</sup> , is set to 0.45, while it is set to 0 _._ 3 in the traded goods sector. These values indicate that the informal (non-tradable goods sector) sector is more intensive in labor than the tradable goods sector. The depreciation rates of capital are fixed to 0.1 in the informal sector and 0.075 in the tradable goods sector. All these values are consistent with studies on Sub-Saharan African countries and reported in Jihad et al. (2012), Cherif and Fuad (2012) and Ag ́enor (2014).

In our paper, we use the empirical evidence based on Mexican data from 1980 to 1994 by Arestoff and Hurlin (2006) to pin-down the parameter that governs the efficiency of public investment _ε_<sup>_G_</sup> . This paper shows that the coefficient of regressing public capital produced (effective investment in our model) on investment expenditures falls between
# **Figure 6: Ingrained income bias and endogeneity**

In a cross-country analysis, the hypothesized effect of natural capital on bond yields may in fact be driven by the unobserved level of income or development. Neglecting _푑푒푣푒푙표푝푖_ will bias the results and overestimate the effect of interest (dashed arrow).

Level of development or income _푑푒푣푒푙표푝푖_ ↗↙ ↘↖ _푛푎푡푐푎푝푖_ −−−−−−−−→ _푦푖푒푙푑푠 푖_ Average natural capital Average bond yields ˆ [The _훽_<sup>ˆ</sup> coefficient in _푦푖푒푙푑푠 푖_ = _훼_ + _훽_<sup>ˆ</sup> _푛푎푡푐푎푝푖_ can be interpreted as the percentage point change in the average yield associated with a 1% increase in average natural capital. The standard errors are reported below the coefficients. The stars indicate the level of significance with *** p<0.01, ** p<0.05, * p<0.1.] The statistical results suggest no substantial relationship for A-rated issuers, but provide strong evidence that resource-rich issuers with B-ratings tend to pay higher yields.

# **3.2 The ingrained income bias**

From a long-term growth perspective, this finding makes sense. Natural wealth is an essential input for long-term, sustainable growth. This raises the (natural) rate of interest which drives up inflation expectations and ultimately bond yields. Thus, countries with higher resource endowments are reasonably associated with higher growth potential. This is consistent with the fact that the wealth data was constructed with the explicit goal of capturing long-term growth potential in mind.

Although this finding is intuitively appealing, its simplicity does not rule out any alternative explanation. Higher yields can also indicate higher default risk, for example due to the resource curse. Natural wealth alone does not necessitate a positive economic growth path. In a system with little protection of private property rights and weak rule of law, resource rents can be misappropriated. This can lead to corruption and in extreme cases constitute a source for armed conflict. Neither
3 anfl the use of price deflators for government output. Section I then reviews available data on government expenditures for OECD and developing nations. Both a long term perspective, provided by several OECD economies, and contemporary experience, essentially since 1960, are presented.

In Section II, the determinants of the growth in government are considered. This literature covers demographic demands for expenditure growth, the changing relative prices of public vis-a-vis private goods, the income elasticity of public goods, and the arguments of the gs : choice school. Most of these explanations have been directed at unde4., .1ding trends in industrialized nations, and little has been written on the relevance of these explanations for developing nations. This section briefly speculates on the determinancs of government growth in developing countries against the backdrop provided by results on advanced economics.

# I. Measuring the Growth in Government Expenditures

# A. MEASUREMENT ISSUES

# 1. Defining the scope of the public sector.

Depending upon the specific question to be raised, government expenditures may or may not be a suitable proxy for the size of government. Any nominal valuation of government, be it an expenditure or revenue measure, implicitly assumes that government's role as a direct economic agent is what is to be assessed. Government's impact as a regulator of economic activity, through such macro or micro instruments as monetary policy or the tariff structure, clearly will not be captured by a nominal index which defines a government's size and influence ex2lusively in budgetary terms.1
region, which is itself derived from digitized maps of the footprints of _all_ buildings in the region from very high-resolution satellite imagery. This allows for a consistent definition of urban areas across all countries in the region, thereby overcoming the measurement problems that arise from relying on official definitions of urban areas, which vary widely across both SSA countries and countries globally (World Bank 2009, Roberts et al. 2017, Dijkstra et al. 2021).

As part of its analysis of urbanization for SSA, the paper presents evidence on key features of the urban landscape, both across and within urban areas. Looking across urban areas, the paper provides evidence on, _inter alia_ , aggregate differences in levels of urbanization between countries, numbers of urban areas, the share of land they occupy, their population densities, and the structure of urban hierarchies as captured by, for example, Zipf’s law, as well as on how these features of urbanization correlate with both a country’s size (i.e., population) and level of development. Meanwhile, looking within cities, it provides evidence on key empirical regularities such as population density gradients and the number of (sub-)centers.

The paper contributes to three interrelated literatures. First, it contributes to a nascent literature that provides new evidence on patterns of urbanization in Africa by leveraging satellite data. This includes a recent paper by Henderson, Peng, and Venables (2022) which uses data from the European Union’s Global Human Settlements built cover data set, _GHS-BUILT_ , to examine patterns of settlement growth across SSA between 1975 and 2014. Henderson et al. identify a rapid growth in the number of settlements – from about 47,500 to around 111,500 – with evidence of complementary (competitive) patterns of growth for nearby settlements of different (similar) size.<sup>4</sup> They also empirically reject Gibrat’s law, which underpins Zipf’s law in Gabaix (1999), finding that built-up area
the standard error is larger and the effect is not significant. If we look at column (3), the point estimate is smaller and insignificant. However, if for efficiency reasons we restricted the -0.05 time effect to be zero, the results would suggest a positive and significant impact on these children. When we focus on program-eligible children, the effects are bigger and highly significant.

Next, we analyze the second group of communities (Panel B). Since this group is relatively betteroff than the first one, we might expect to see some differences once we focus on more disadvantaged children. Results in columns (2) to (4) confirm this intuition. The table shows that the estimated effect of partial exposure is now larger and statistically significant. This suggests that, unless differential time effects for this community group were positive and sufficiently big, Oportunidades seems to have had a positive effect on these children’s height.

In contrast, the bottom panel suggests that conclusions do not change when we analyze communities incorporated after 2002 (Panel C). Regardless of the sample stratification used, there is no evidence of a positive impact on these children. Even though impact effects are not statistically significant, point estimates for the last group of communities are negative and quite large.

We search for evidence on negative shocks that might be behind the estimates, particularly for the third group of localities. As mentioned in the previous section, we obtained from Oportunidades records a list of localities in which some households were enrolled in the program through a special process after having been affected by a natural disaster. This would bias our estimates downwards, as there would be perfect correlation between program entry (positive shock) and the negative shock. It turns out that only 3 MxFLS localities are in this list, and all results are insensitive to including them or not.

26
where _t_ indicates the years since the segment quality peak. We fit _b_ 0 and use this value to compute a _δ_ to describe the decay of all roads. When computing the slope of a single segment, we weigh the slope by the number of images in that year. This ensures the model puts more weight in places where there are more measurements. The slopes of each segment ( _b_ 0) are combined into one discount factor using a weighted average, where the weight is the number of images in a particular segment.

To illustrate our data _y_ , Figure 7 zooms into segments 180, 181, and 182 and shows the IRI classifier output for each image for three years. The edge of each segment is marked with a light blue point. Each colored pixel represents one of the tiles fed into the deep-learning classifier, and the color represents the predicted probability, where a darker green tile means a better road and a darker red tile means a worse road. Road quality declines overall. This is visible through the increasing red shades on the right. The exception is segment 180, which is the first segment. This segment first declines in quality from 2012 to 2016 and then increases in quality again in 2021.<sup>8</sup> The resulting data is shown in Appendix Figure A4. From this data, we estimate the road discount factor using the equation above as (0 _._ 735 _±_ 0 _._ 05). This implies that, after three years, 40 percent of a rehabilitated road would still be of the same quality as just after rehabilitation, and 60 percent would be unusable.

# **3.4 Mining and Natural Resources**

As discussed in section 2.2, mining can be an important mediating factor affecting both roads and conflict events. We collected information on the location of both large and artisanal mines in eastern DRC. We used data from the World Bank and S&P Global to compile a list of mining sites
**Appendix Table 5: Households’ eligibility for transfers at baseline based on survey data.**

|||**Qu**<br>|**intiles of Pr(Tr**<br>|**eat)**<br>|||
|---|---|---|---|---|---|---|
||Q1|Q2|Q3|Q4|Q5|Total|
|**Panel A: HH is eligible for transfers**<br>|||||||
|Treatment|0.003|0.027|0.59|1|1|0.545|
|Control|0|0.001|0.464|1|1|0.517|
|**Panel B: Eligibility criteria**|||||||
|Dependency ratio>=1.5|||||||
|Treatment|0|0.003|0.281|0.758|0.977|0.415|
|Control|0|0|0.217|0.783|0.978|0.424|
|Four or more children household|||||||
|Treatment|0|0|0.262|0.385|0.919|0.321|
|Control|0|0|0.233|0.413|0.899|0.332|
|Single parent household|||||||
|Treatment|0|0.018|0.034|0.07|0.066|0.039|
|Control|0|0|0.026|0.077|0.079|0.039|
|Single female household|||||||
|Treatment|0.003|0
ects of formalization policies focused on the reduction of formal sector entry costs are probably limited. On the other hand, human capital policies that increase the skill of informal entrepreneurs may have signicant eects. We will return to these issues when we discuss the evidence on formalization policies in section 5.

# 3.3 Productivity dynamics

Using data from the World Bank Enterprise Survey, Barbosa Filho and Correa (2017) show that the average size of Brazilian rms is small in comparison to other emerging economies.<sup>13</sup> The average number of workers per rm in Brazil is similar to the one found in Russia, but considerably smaller than the average size in Chile, Colombia and China. The authors also note that older rms are not much larger than young rms in Brazil. Moreover, productivity does not increase with age. This suggests that the competitive selection mechanism is weak in Brazil. As a result, more productive rms do not grow and low-productivity rms stay 13This evidence was obtained from the same dataset that Barbosa Filho and Correa (2017) used to compare the distribution of labor productivity in Brazil to other emerging economies.

13
--|---|
|Variable nam e<br>D e...nition<br>Avg. tari¤ rate<br>Applied tari¤ rate to m anufactured products, weighted m ean (in % )<br>FD I in‡ows (log)<br>Logarithm of net foreign direct investm ent in‡ows (in m illions of USD )<br>D istance to G VC hubs (log)<br>Logarithm of sum of distance to China, G erm any and the US (capital city-to-capital city)<br>Political stability index<br>Political Stability and Absence of Violence/Terrorism : Estim ate<br>D om estic industrial capacity (log)<br>M anufacturing value added (in current USD ) obtained by multiplying nom inal G D P with the share of<br>m anufacturing in value-added<br>Low-skilled lab or share / real G D P (log)<br>Logarithm of the share of em ploym ent of skill level 1 (low) according to the International Standard<br>Classi...cation of O ccupations in total em ploym ent<br>https://www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm<br>M ed/High-skilled lab or share / real G D P (log)<br>Logarithm of the share of em ploym ent of skill levels 2-4 (m ed-high) according to the International Standard<br>Classi...cation of O ccupations in total em ploym ent<br>https://www.ilo.org/public/english/bureau/stat/isco/isco08/index.htm<br>Rents from resources % G D P<br>Total natural resources rents as a p ercentage of G D P<br>Land / real G D P (log)<br>Logarithm of
address this issue in a non-classical and non-linear measurement error model, allowing for a general relationship between measurement errors of GDP and nighttime light growth, statistical capacity, and geographic locations.<sup>5</sup> In this paper, we develop a novel approach to estimate the quarterly elasticity between VIIRS nighttime lights and GDP, employing the effective number of nighttime light observations to identify the relationship. The latter provides information to quantify the measurement errors in the lights data, which greatly simplifies the estimation.

In our applications, we construct a light-adjusted measure of economic activity to cross-check official estimates similar to Henderson, Storeygard, and Weil (2012) and Martinez (2021). We check whether measurement errors in the long-term, short-term, and during the COVID-19 pandemic vary systematically with country characteristics. In addition, we study the spatially heterogeneous impact of the pandemic and hence contribute to the large and growing empirical literature on the economic impact of COVID-19.<sup>6</sup>

# **3 Data**

## **3.1 Nighttime Lights**

Nighttime light data have changed over two different generations of satellites. The first generation of nighttime light data, based on the Operational Linescan Sensor (OLS) onboard the DMSP satellites, covers 1992 to 2013 at annual frequency. The second generation, based on the VIIRS Day Night Band (DNB) onboard the Joint Polar Orbiting Satellite System, covers April 2012 to the present at monthly frequency. Compared with DMSP-OLS, VIIRS nighttime lights have higher resolution and benefit from much improved low light imaging (Elvidge, Baugh, Zhizhin, and Hsu, 2013).<sup>7</sup> This paper focuses on VIIRS nighttime lights at quarterly frequency. To obtain quarterly nighttime lights, we use the monthly version of the cloud-free DNB composites, which excludes data impacted by stray light between April 2012 and December

> 5In the working paper version, Hu and Yao (2021) also provide an elasticity for VIIRS data. However, different from
1979-2022. The daily data is available from 1990-2021. The data is processed to measure annual precipitation totals within each sub-national administrative level 2 (ADM2) unit that contains at least one firm in the ES data. Annual shocks are defined as instances when the annual precipitation within an ADM2 unit is at least one standard deviation above or below that unit’s long-run average (1990-2021). About 3.8 percent of the sample faced a negative precipitation shock while 19.1 percent of the sample faced a positive precipitation shock. For reference, Benincasa (2024) finds that 9.1 percent of firms across Europe and MENA self-reported monetary losses from extreme weather events (this measure includes storms, droughts, floods and landslides). The same measure for the sample of MENA economies in this study is about 8 percent.

# **2.2 Empirical Approach**

The empirical approach exploits cross-sectional variation in the ES data combined with precipitation shocks at the ADM2 geographical unit, while accounting for sub-national ADM1 or country fixed effects. The main analysis estimates the effect of precipitation shocks at the ADM2 level on sales at the firm level while controlling for temperature and other firm-level controls. The following regression is estimated: Where: _i_ indexes firms, _a_ indexes sub-national ADM1 units, g indexes ADM2 units, s indexes sector (2 digit ISIC level) and _t_ indexes survey-years. Note that ADM2 is a more geographically disaggregated unit than ADM1. There is a still a possibility that some of the shocks could be colinear with ADM1 fixed effects. Therefore we substitute the ADM1 fixed effects for country fixed effects across the estimations to show the robustness of the findings. For the cross-sectional estimations involving the green module, we deploy country fixed effects given the lack of variation over time. i i is the log of sales (in USD). i ii YY SSho oo 7
) the presence of escape clauses, and (vi) flexibility features are associated with less procyclicality than non-FR countries, highlighting the relevance of adequate design of national FRs to mitigate procyclicality.

# **[insert Table C.1 and Figure C.1]**

# **_Top-down solution: Fiscal councils_**

The other potential top-down solution we explore is another fiscal institution, namely the fiscal councils. Fiscal councils (FCs) are defined as independent, non-partisan agencies with an official mandate to assess fiscal policies, plans, rules, and performance (Debrun et al., 2013). They do not have a direct role in setting policy instruments, but they can influence fiscal behavior through three main channels. First, by fostering transparency of fiscal policy, an FC might reduce the rationale for voters to “starve the Leviathan.” Second, they can increase the reputational costs for politicians on unsound policies and broken commitments. Third, they can provide direct inputs to the budget process, with forecasts or assessments of structural positions; as suggested in the previous paragraph they can close technical loopholes that allow governments to circumvent numerical fiscal rules (Debrun et al., 2017). Taking advantage of the FC data set from the Fiscal Affairs Department, we check (i) whether the FC

> 13 We account for numerical fiscal rules. For a literature review on procedural rules, see Alesina and Perotti (1999). 14 On procyclical bias, a petition signed by 1,100 economists and 11 Nobel economists in the New York Times claimed that attempts to strictly keep the budget balance (in US states) would aggravate recession (see Levinson, 1998).

19
banks on the restructuring of USD 11.2 billion of foreign debt.<sup>9</sup> The news sent Zambia’s bond spread surging and ultimately pushed the country into default when the government missed a USD 42.5 million coupon payment on one of its Eurobonds in October 2020.

The uncertainty over the true scale of Zambia’s debt stock caused long delays in the ensuing debt resolution process. Creditor committees declined Zambian requests for a debt standstill and a restructuring proposal over concerns about the “lack of transparency regarding the country’s other external debts’’.<sup>10</sup> After several months of data reconciliation, Zambian authorities eventually published revised debt statistics that confirmed investor concerns and led Zambia’s new president to admit that the “hole is bigger than expected”.<sup>11</sup> Four years later, as of June 2024, the restructuring had not yet concluded.<sup>12</sup>

> 8See Africa Confidential (2018, April 6) _“Into the valley of debt”_ and Bloomberg (2018, April 8) _“Zambia’s bonds plummet on concern it’s pulling a Mozambique”._

> 9See Financial Times (2020, April 1) _“Zambia’s bonds drop on expected restructuring”._

> 10See Financial Times (2020, November 13) _“Zambia on brink of default after lenders reject debt relief request”_ and Financial Times (2020, September 30) _“Bondholders balk at Zambia’s plan to delay debt payments”_ .

> 11BBC (2021, September 1) _“Zambian President Hichilema inherits ‘empty treasury’”_ .

> 12Negotiations are still ongoing with non-bonded commercial creditors representing a quarter of the country’s external debt.

6
and the domestic value added ratio (DVAR) in exports is defined as the share of DVA in exports: However, the domestic materials used in the production of exports may contain foreign contents, which should not be included in DVA. In contrast, the imported materials used in the production of exports that may contain domestic contents, which should be included in DVA. We define the foreign content embedded in domestic materials used for exports, and the domestic content embedded in imported materials used for exports, as _δ_<sup>_F_</sup> and _δ_<sup>_D_</sup> , respectively, expressed in terms of the shares of exports for both. Thus, the DVAR definition is subject to the following adjustment: Wang et al. (2013) provides estimates for foreign content embedded in total domestic production and domestic content embedded in total imports, both estimates can be used to infer _δ_<sup>_F_</sup> and _δ_<sup>_D_</sup> .11 There are two trade regimes in China, processing trade and ordinary trade (non-processing trade).12 Legally, the processing firms are required to use all their imports as imported intermediate inputs for exports, implying that _κ_ = 1, and thus: In contrast, for the ordinary or non-processing exporters, only a fraction of their imports, 0 _≤ κ ≤_ 1, will be used as inputs for the export production, given that these firms also produce for the domestic markets. To construct the DVAR for these firms, proper estimates for _κ_ are needed. Kee and Tang (2016) relies on industry census data for total material and total revenue at firm level to infer _κ_ for each firm. However, while customs transaction data is often accessible, matching it with less accessible industrial census data can be challenging, which limit the broader applicability of their method.13 To address this difficulty, we estimate _κ_ based on (2), allowing for a more direct calculation without requiring matched