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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 data have proven helpful to monitor economic activity (Henderson, Storeygard, and Weil, 2012; Keola, Andersson, and Hall, 2015; Henderson, Squires, Storeygard, and Weil, 2018), to assess the quality of national ac-

> 4During the COVID-19 pandemic, official surveys used to estimate GDP could not be conducted in many countries due to stringent lockdown measures.

3
the year ( _Floresta de Igapó_ ). Finally, 35 percent of Pará’s area is protected, where 75 percent are for Indian use.

Economic growth is an essential component of a poverty reduction strategy (World Bank 2002). In 2000, the GDP of Pará accounted for 1.8 percent of the total Brazilian GDP and 37 percent of the GDP of the North region, equivalent to R$18.9 billion. During 1991-2000, Pará’s economy grew at an average annual rate of 2.4 percent. This is not enough to reduce poverty, given that the population expands at around 2.5 percent per year (see below). Pará’s economy is slightly a dual where, on the one hand, services contribute 49 percent of GDP, accounting for 63 percent of employment and manufacturing, and on the other hand, it contributes 29 percent of GDP, but only 16 percent of jobs (Table 2.2).

|**Table 2.2: Sectora**|**l Trends in Pará**|**’s Economy, 19**|**95-2000**|
|---|---|---|---|
|**Sector**|**Yea**|**r**||
||**1995**|**2000**|**Variation**|
|Agriculture|29.31|23.08|-6.23|
|Mining|3.09|4.08|0.99|
|Manufacturing|14.90|14.48|-0.42|
|Public Utilities|1.44|1.14|-0.30|
|Construction|11.98|11.07|-0.91|
|Commerce|7.30|6.18|-1.12|
|Housing and Food|0.50|0.67|0.17|
|Transport and Storage|1.62|2.12|0.50|
|Communication|0.93|1.91|0.98|
|Finance|1.60|2.70|1.10|
|Rent|6.44|9.35|2.91|
|Public Administration|16.41|17.97|1.56|
|Health and Education|2.02|2.74|0.72|
|Other Services|2.12|2.11|-0.01|
|Domestic Services|0.35|0.40|0.05|
|_Source_: SEOF/ DIEEST/ COPASE.||||

In Pará, export revenues are among the highest in Brazil. Pará’s industrial and trade poles and geographical location allows the state easy access to the foreign markets, such as the U.S. and European markets. In the nineteenth century Pará, was one of the preeminent states of Brazil. Rubber exports were the foundation for the development of the state and the driving force in the economy. Even today exports are far larger than imports. In 2002, the state achieved a trade surplus of US$ 2.0 billion and was the fourth largest exporter of the 27 Brazilian states. Two commodities, iron and aluminum, accounted for 53 percent of all the export revenues of US$ 2.3 billion.

# **PEOPLE AND THEIR LIFESTYLE**

Pará’s population of 6.2 million, 3.7 percent of Brazil’s total, counts many indigenous groups, Caboclos, Quilombolas, and migrants form other states in Brazil, and European descendants. Its people are very knowledgeable of the natural world and have adapted to a lifestyle with difficult access to modern services in many locations. This section is based on own observations, discussions with people in Pará, and Amazon (2002).

7
|villages in the Sahel<br>(Aker and Jack 2023)|deviations compared to<br>the control group|uncultivable land had<br>been restored.||
|---|---|---|---|
|No-till and winter cover<br>crops for winter wheat,|The gross profit margin<br>outperformed||SOC, measured at 0–<br>30 cm depth, increased|
|maize, and soybean, in|conventional tillage by||by 2.75% annually|
|Piacenza, Po Valley,|6% to 200% over 2011||from 2011 to 2020 and|
|Northern Italy|to 2020.||didn’t vary by the type|
|(Lorenzetti and Fiorini<br>2024)|||of cover crop.|

# **<mark>3.1 On Productivity</mark>**

Research has shown that certain regenerative agricultural practices or their combination can enhance crop productivity, particularly in dry climates. A global meta-analysis involving 610 studies found that employing no-till alongside residue retention and crop rotation significantly boosts rainfed crop productivity in dry climates (Pittelkow, et al. 2015). Additionally, rainwater harvesting techniques have demonstrated a notable increase in the value of agricultural production, ranging from 0.12 to 0.15 standard deviations compared to the control group in the Sahel (Aker and Jack 2023). In Malawi and Zimbabwe, the adoption of practices such as crop residue utilization, crop rotation, and minimum tillage has been associated with a substantial 12%-18% increase in cereal productivity (Makate, et al. 2019). In addition, some evidence suggests that a combination of regenerative agricultural practices can create a larger impact on productivity gains than being adopted in isolation (Tambo and Mockshell 2018).

However, most of the literature only focuses on the benefits of adopting regenerative agricultural practices, such as an increase in yield and household income, without carefully evaluating the costs and trade-offs associated with adopting each regenerative agricultural practice. For example, adopting regenerative agricultural practices may lower input costs because of a reduction in fertilizer use in some developed countries (LaCanne and Lundgren 2018), but may also increase costs of adopting machines and other equipment. Evaluating the costs and benefits associated with each practice based on local conditions is critical to determine whether certain practices should be recommended to farmers. Also, it takes time for the soil to restore its nutrients and health through regenerative agriculture and productivity may drop during the restoration process in the short term. Thus, farmers may face the trade-off between short-term 12
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**Table 5 Output growth and commodity prices**

||**Depen**<br>|**dent variabl**<br>|**e: GDP grow**<br>|**th**<br>|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||EMDEs|||Full sample||
|Commodity export price<br>index|0.085***|0.063***|0.080***|0.024|0.018|0.026|
|Terms of trade||0.029|||0.026||
|GDP growth (t-1)|||0.367***|||0.366***|
|Commodity export price<br>index x EMDE||||0.062**|0.046*|0.054**|
|_F_-test||||***|***|***|
|Observations|381|364|370|533|436|518|
|Countries|11|11|11|15|15|15|

_Source_ : World Bank.

_Note:_ Panel least squares with country fixed effects. All variables are in log-differences. The estimates in columns (1)-(3) are based on the sample of emerging market and developing economies (EMDEs) only while those in columns (4)-(6) are based on the full sample that includes advanced-economy commodity exporters. _F_ -test in columns (4)-(6) evaluates the joint significance of commodity export price index and the interaction of the index with the dummy variables for EMDEs. *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. GDP = gross domestic product.

49
Turning back to the DHS data, now that we have an indicator of fever and a measure of wealth, we can look at the relationship between the two within the two large regions of Sub-Saharan Africa. First, consider the geographic distribution of fever and poverty. Overall the numbers do not support the notion that there is a strong association between the incidence of fever and the wealth of the area in which the child lives. Figure 1 shows the incidence of fever in the past two weeks among children under three and of the proportion of the population in the by poorest quintile by geographic subregions. _15_ While there are some subregions that are both poor and have a lot of fever, there is not an overwhelming resemblance between the figures in either of the regions. This impression is confirmed by examining the correlation between the two variables in each region (Table 5).

**Figure 1. Fever and poverty in Sub-Saharan Africa: Proportion of children under 3 who had a fever in the past two weeks and proportion of population in the poorest quintile**

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**Table 3: Selected characteristics of household enterprises in rural Malawi**

||Rural<br>|Malawi<br>|Non<br>|Poor<br>|Po<br>|or<br>|p-value<br>|difference<br>|
|---|---|---|---|---|---|---|---|---|
|Characteristics of households’ enterprises|2010|2013|2010|2013|2010|2013|2010|2013|
|Age of enterprises (years)|11.1|9.0|11.7|9.0|12.9|9.1|0.250|0.821|
|Outside partner (%)|3.4|3.2|4.5|3.0|0.7|3.7|0.008|0.727|
|Business operating premises (%)|||||||||
|Home|47.0|43.3|48.1|44.4|44.6|39.8|0.592|0.356|
|Market place and commercial area shop|31.5|38.0|29.8|36.6|35.5|42.8|0.285|0.198|
|Roadside and other areas|21.4|18.6|22.1|19.0|19.9|17.4|0.671|0.712|
|Formal registration (%)|7.1|7.9|9.1|9.3|2.4|3.3|0.003|0.008|
|FBPE<sup>a</sup>(%)|14.3|14.3|10.1|13.4|23.7|17.5|0.001|0.282|
|Access to electricity (%)|8.0|13.6|13.8|15.8|0.0|0.0|0.141|0.069|
|Number of enterprises per household (1 to 4)|1.1|1.1|1.0|1.1|1.1|1.0|0.907|0.027|
|Owner if household head (%)|73.6|69.5|75.6|72.3|69.1|60.4|0.233|0.008|
|Age of owner (%)|38.3|38.5|37.4|38.4|40.4|39.0|0.056|0.651|
|Education of enterprise owner (%)|||||||||
|None|77.2|74.1|74.7|70.3|82.8|86.6|0.116|0.000|
|PSLC<sup>b</sup>|10.2|12.7|9.6|14.2|11.5|8.0|0.673|0.009|
|JCE<sup>c</sup>|8.1|7.9|9.6|8.7|4.7|5.1|0.078|0.091|
|MSCE<sup>d</sup>|3.7|4.5|4.9|5.7|1.0|0.4|0.010|0.000|
|Non-university diploma|0.5|0.5|0.7|0.6|0.0|0.0|0.117|0.044|
|University diploma|0.4|0.4|0.5|0.5|0.0|0.0|0.311|0.135|
|Post-graduate degree|0.0|0.0|0.0|0.0|0.0|0.0||0.322|

_Source:_ Malawi IHS3 panel sample (2010) and IHPS (2013).

_Note:_ a Forest Based Products Enterprise. b Primary School Leaving Certificate. c Junior Certificate Examination. d Malawi School Certificate of Education Examination 28
**Liquid Liabilities to GDP** is a traditional indicator of financial depth, already used by King and Levine (1993) in their seminal paper on finance and growth.   It equals currency plus demand and interest-bearing liabilities of banks and other financial intermediaries divided by GDP. This is the broadest available indicator of financial intermediation, since it includes all banks, bank-like and nonbank financial institutions.<sup>4</sup> There is a wide cross-country variation in Liquid Liabilities to GDP, as shown in Figure 1, ranging from 395% in Luxembourg to less than one percent in Sudan.  However, there is an even larger variation in the absolute size of financial system, as illustrated by **Liquid Liabilities in USD** (Figure 2).   On the one extreme, there are financial systems with trillions of USD, such as Japan or the U.S., on the other extreme there are small and poor countries with financial systems smaller than the size of one small bank in developed countries.  In Sudan, for example, total liquid liabilities are barely 40 million U.S. dollars, while they are 73 million U.S. in Guinea-Bissau.

**Currency outside Banking System to Base Money** is an indicator of monetization of the economy, as it shows which share of base money is not held in the form of deposits with the banking system. Not surprisingly, low-income countries had the highest ratio of Currency outside Banking System to Base Money with a median of 39% in 2007, while upper-middle income countries had the lowest ratio, with a median of 26% (Figure 3). The ratio has decreased over the past decades, from 40% in 1980 to 30% in 2007 (Figure 4). The level and change in currency outside the banking sector is often used as basis for estimations of the size of the informal sector (Schneider and Ernste, 2000).

**Financial Systems Deposits to GDP** is the ratio of all checking, savings and time deposits in banks and bank-like financial institutions to economic activity and is a stock indicator of deposit resources available to the financial sector for its lending activities.  The ratio varies positively with the income level of countries (Figure 3). The median across countries has shown an increasing trend since 1980 when it stood at 27%, reaching 51% in 2007.  The database also contains an indicator limited to deposits of deposit monetary institutions – **Bank Deposit to GDP** .

> 4 The International Financial Statistics (IFS) of the IMF distinguishes between three groups of financial institutions. The first group comprises the central bank and other institutions that perform functions of the monetary authorities. The second group, deposit money banks, comprises all financial institutions that have “liabilities in the form of deposits transferable by check or otherwise usable in making payments” [IMF 1984, 29]. The third group – other financial institutions - comprises other banklike institutions and nonbank financial institutions. These are institutions that serve as financial intermediaries, while not incurring liabilities usable as means of payment.

4