rafmacalaba/data-use-mentions-extended huggingface dataset:
# **II. The Power of the Network and Firm Performance**

In the presence of economic instability, market imperfections, and weak government-provided legal institutions, the power of the African business network rests partly on the exchange of information through it and on group enforcement, and partly on the ready ability of the group to support transactions that benefit from relation-based governance, such as financing, sales, and distribution to customers outside the immediate neighborhood. Evidence of such network externalities (or social capital) in SSA has been provided by Barr (2000), Fafchamps (2000, 2004), Biggs, Raturi and Srivastava (2003), and Fisman (2001,2002).

Community connections play a crucial role in the membership of African business networks. The RPED surveys show that ethnicity is a strong indicator of network activity in manufacturing. Ethnic minority groups, for example, dominate many of the major manufacturing activities. In East Africa, for example, business networks of Indian ethnicity concentrate in segments of light manufacturing and import/export trade. In Southern Africa, European business networks control much of the upstream activities in manufacturing and mining. And in West Africa, Lebanese business networks are heavily involved in import/export trade and parts of the wood industry. Across the region indigenous-African, ethnically-based networks are found mainly in agricultural and natural resource activities – the Luo, in Kenya, are networked in the fishing industry and the Ashanti, in Ghana, in the cocoa industry. African ethnically-based networks can also be found in small-scale industrial activities, such as metal working, furniture, food processing and clothing. Fafchamps (2004) argues that the distinct patterns of ethnic concentration in business, observed across SSA, can be explained to a great degree by a restricted entry process in business networks and by network externalities. Since network externalities bestow comparative advantages in business on network members, important ethnic communities earn rents and become dominant in particular segments of the economy. Networks reinforce themselves through a referral process and statistical discrimination.

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# **Policy Research Working Paper Series**

||Title|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|---|
|V\APS23|28<br>Social Transfers and Social<br>Assistance: An Empirical Analysis<br>Using Latvian Household Survey Data|Branko Milanovic<br>|April 2000|P. Sader<br>33902|
Figure 2 displays the total amount of loans taken by treatment for each of our four treatments where loans are available. Loans are expressed as percentage of endowments to account for differing endowment levels in the inequality treatments. The highest amount of loan taking is recorded in the baseline condition (endowments are equal, and expenditure is not observable), while the lowest is found in the treatment where endowments are unequal, and expenditure is not observable. Comparing loan-taking in these two treatments, it is clear to see that the house money effect is clearly at play here. Loans are far less prevalent when subjects earn their endowments, relative to when subjects are given equal endowments ( _p<0.01_ ).

Importantly, however, as with expenditures, observability reduces loan-taking when endowments are equal ( _p<0.05_ ), but increases loan-taking when endowments are unequal, and effort based ( _p<0.05_ ). In other words, a cursory look at the graph reveals that when consumption can signal ability, subjects are more likely to take on loans to front load consumption.

<!-- Start of picture text --> Loans taken (% of endowment)<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Equal (unearned) endowment Unequal (earned) endowment<br>Expenditure is NOT observable Expenditure is observable<br>Loans token (% of endowment)<br><!-- End of picture text -->

**Figure 2: Loans taken by treatment**

Note: The error bars reflect the standard error of the mean. The figure reports data from the treatments with loans.

As with expenditures, we use a simple OLS framework with the dependent variable as the total tokens taken in loans by the subject, expressed as a percentage of their total endowment. Specifically, we test the hypothesis that observability increases loan-taking
to which other crucial skills, specifically cognitive skills (general knowledge and mental abilities) and socioemotional skills (behaviors and attitudes to manage emotions, relationships and personal goals), are needed to learn and manage digital technologies required in the workplace.

Previous work using data from high-income countries provides evidence of the complementarity among digital, cognitive, and socioemotional skills. Research using the PIAAC surveys of adults in 30 countries (mostly high-income) shows that workers using more digital skills at work also have higher basic cognitive skills (e.g. numeracy) and perform more tasks that require cognitive and socioemotional skills, such as management and communication, accountancy and sales, and negotiation (OECD 2016, 2019). ICT occupations, such as software and application developers and database professionals, require more advanced digital skills (e.g. programming). However, the PIAAC data also show that workers in ICT occupations perform many tasks that require cognitive and 2
- Naser, A.M., Wang, Q., Chellaraj, G., Shamsudduha, M. and Joseph, G. 2019b. “Modeling Groundwater Salinity and Neonatal and Infant Mortality from the Bangladesh Demographic Health Survey 20002014.” Rollins School of Public Health, Atlanta, Georgia, USA.

- Naser, A.M., Higgins, E.M., Arman, S., Ercumen, A., Ashraf, S., Das, K.K., et al. 2018a. “Effect of Groundwater Iron on Residual Chlorine in Water Treated with Sodium Dichloroisocyanurate Tablets in Rural Bangladesh.” _American Journal of Tropical Medicine and Hygiene,_ 98:977-983.

- Naser A.M., Rahman M, Unicomb L, Doza S, Uddin MN, Robin RA, et al., (Eds). 2018b. _Drinking Water Salinity Categories and Lower Blood Pressure: Evidence from Coastal Bangladesh._ ISEE Conference Abstracts.

- Naser, A. M., Rahman, M., Unicomb, L., Doza, S., Ahmed, K.M., Uddin, M.N., et al. 2017. “Drinking Water Salinity and Kidney Health in Southwest Coastal Bangladesh: Baseline Findings of a Community-Based Stepped-Wedge Randomized Trial.” _Lancet,_ 389:S15.

- Naus, F.L., Schot, P., Groen, K., Ahmed, K.M. and Griffioen, J. 2019. “Groundwater Salinity Variation in Upazila Assasuni (Southwestern Bangladesh), as Steered by Surface Clay Layer Thickness, Relative Elevation and Present-Day Land Use.” _Hydrology and Earth System Sciences,_ 23:14311451.

- Nickson, R., McArthur, J., Burgess, W., Ahmed, K. M., Ravenscroft, P. and Rahmann, M. 1998. “Arsenic Poisoning of Bangladesh Groundwater.” _Nature,_ 395:338.

- Park, Y., Lee, J-Y
results, 31% of microenterprises employees and 12% of employees in Poland receive envelope payments. Envelope payments equal 6% of Poland's labor income and 1.6% of the Polish GDP.

**Research done for other countries clearly shows that underreporting of top incomes in survey data significantly biases the true measure of income inequality downwards; our analysis shows this is also the case in Romania** . In Table 4, we compare the measures of income inequality in the EU-SILC dataset, tax data, and imputed EU-SILC and find that this is also the case in Romania. Our reference income variable is gross labor income. We restrict our sample to employees because we do not have complete administrative tax data for self-employed. We measure income inequality on the individual level. Income inequality is significantly higher in tax and imputed tax data than in survey data. In the original

21
# Introduction

The aim of this study is to examine the differential impact of disasters and climate shocks on populations in conflict-affected regions, specifically investigating the repercussions of flooding in conflict versus non-conflict areas. Using remote sensing technology, we attempt to overcome the challenge of data scarcity in conflict-affected countries, allowing us to account for short-term impacts of recent disaster and climate shock events. Despite the inherent limitations of using nightlight intensity as an economic activity indicator, it provides an empirical foundation for the analysis and enough observations for an ex-post quasi-experimental impact evaluation. We employ a difference-in-difference econometric approach, using satellite imagery of nightlight radiance alongside geospatial data on flood and conflict events. This methodological framework is applied to assess the aftermath of the March-April 2019 Floods in Mozambique following Cyclones Kenneth and Idai, as well as the 2022 floods spanning July to October in Nigeria.

Results show significant disparities in the effects of disasters and climate shocks between conflictaffected and non-conflict-affected areas. Specifically, we observe a more pronounced decline in economic activities in conflict-affected regions.

attention to the interconnectedness of conflict and disasters and climate shocks, outlining the methodology and empirical strategy derived to estimate such ex-post impact. In the second section, we present the results and supporting data derived from the study, including the case studies on Mozambique and Nigeria. Finally, we discuss limitations as well as broader implications before concluding.

# Context: Flood impact and conflict affected population (Literature)

## 1.

Quantitative economic analyses have frequently used nightlight radiance as proxy for economic activity (Chen & Nordhaus, 2011; Henderson et al., 2012). These have also been used to estimate the impacts of weather variability and disasters and climate shocks (Bertinelli & Strobl, 2013; Elliott et al., 2015; Felbermayr et al., 2022; Heger & Neumayer, 2019; Miranda Montero et al., 2017) and, more specifically, floods (Kocornik-Mina et al., 2020) _._ Most analyses using nightlight data usually demonstrate a negative impact of disaster and climate shocks on
imagery, purposefully collected archival (1983-2010) rain gauge data from meteorological agencies and other sources and GTS gauge data. Rainfall estimation is based on cold cloud duration (CCD) inferred from TIR and calibrated using gauge data within discrete calibration zones.

Validation of TAMSAT found a mean underestimation of rainfall of approximately four millimeters per dekad, though the bias was not always negative (Tarnavsky et al., 2014). Due to differences in methodology from CHIRPS and ARC2 precipitation products, TAMSAT is not affected by inconsistency in gauge data inputs. This makes it suitable for placing rainfall variability in the context of a long-term climatology and thus detecting unusually wet or dry conditions.

Data and technical documentation are available for download from `http://www.tamsat.org. uk/data/` .

# **A.2 Defining Growing Season**

We define growing season following the FAO crop calendar for each country. Table A1 presents details for each country on the growing season used, as well as whether that season spans years and whether it is unimodal or bimodal. Remote sensing data used in our analysis follows the defined growing season in each respective country.

Of the six countries, two (Malawi and Tanzania) span calendar years, which means that the growing season begins in one year and stretches into the year that follows. Take, for example, Malawi. The growing season in that country begins on 1 October and ends on 30 April. This means that it would begin 1 October 2021 and would end 30 April 2022.

Similarly, of the six countries, two (Nigeria and Uganda) are bimodal. The season modality designates whether different regions within the countries have different growing seasons. In both Nigeria and Uganda, the northern part of the country has a different growing season from the southern part of the country. In these cases we designate the modality of the season, and also provide the growing season dates for both regions.

103
_μ_ m, a substitution is made. An alternative criterion for identifying dust events is when the PM1/10 ratio < 0.25 (Jaffe _et al._ , 2023). Comparing the two Jaffe specifications, we find that approximately 86% of hourly values are the same. Since the monitor that lacks the 0 _._ 3 _μ_ m and 5 _μ_ m values is one of the only ones functioning in 2020, we use this alternative method for generating the Jaffe calibration in order to be able to conduct the analyses of the COVID-19 mobility policies implemented in 2020.

These calibrations both use data from the U.S. where conditions may differ substantially from Dakar, leading to a different relationship between the data from low-cost monitors and regulatory-grade monitors. For example, the lack of paved roads may affect the amount and type of dust particles in the air, along with different fuel standards, high levels of construction, and the proximity to the Sahara Desert. Therefore, we also produce a correction based on calibrating the Purple Air data using data from local regulatory-grade monitors. We co-located two of the PA monitors with two regulatory-grade monitors in the city. One was located in Bel Air and provided overlapping data from July 21, 2020, to April 17, 2021.<sup>11</sup> The second was located in Pikine and provided overlapping data from April 17, 2023, to December 15, 2023.<sup>12</sup> To generate the calibration formula, we use a lasso method with a linear model and a five-fold crossvalidation. We include the Purple Air PM2.5 value along with humidity and temperature averaged across

> 11Some additional dates are missing within this period, most notably October 26-December 31, 2020.

> 12Again, there are some missing dates within this period including all of August and October.

11
all the information available in the dataset, we imputed missing values in the data using a simple median imputation method. This method assigns the median of the available observations at the lowest possible level, i.e. either the class, school or country median, for any missing observation. In this procedure, we used sampling weights to take account of the survey structure of the PIRLS dataset (see Section 3 below). In the estimated regressions, we include imputation dummies which ensure that none of the substantive results are driven by imputed rather than original data, and we do additional robustness tests of the imputation data (again, see Section 3 below).

7
_β_<sup>ˆ</sup> ) across cohorts partly reflects the improvement in the education systems and policies in many countries in the region. The education systems in Sub-Saharan Africa expanded substantially after independence in the 1960s, which almost doubled primary-school enrollments in many of the countries (Thakur, 1991). The expansion of primary education was facilitated through the expansion of public education. Government expenditure on education grew substantially during the period (UNESCO, 1970). During the decade, we observe a decline in intergenerational educational persistence in almost all countries in our sample (see, for instance the Comoros, Ghana, Guinea, and Nigeria estimates in table 4). In the 1980s, the recurrent balance of payment failures and economic regression limited the public expenditure on education. We observe a rebound in the intergenerational persistence rate in our sample countries. A revival of public education funding occurred again in the 1990s and the education systems in many countries in the region underwent dramatic policy changes. One of the most dramatic educational policy changes, for instance, was the abolition of primary school fees in Ghana, Malawi, and Uganda in the 1990s and in Benin, Burundi, Lesotho, Liberia, Mozambique, Rwanda, Sierra Leone, Tanzania, and Zambia in the 2000s (UNESCO, 2011). In line with this, we observe a decline in intergenerational elasticity among the youngest cohorts in all countries except Malawi and Tanzania. The positive relationship between public education expenditure and individual educational attainment has been extensively documented in the literature on both developing and developed countries (Black and Devereux, 2011). Hertz et al. (2007) also provides a survey of the existing literature that reports changes in education policy and intergenerational education mobility. There is also empirical evidence that higher public expenditure on primary education is boosting education mobility across generations in many countries. Thus, it is plausible to hypothesize that our result of rising educational mobility across cohorts using intergenerational education elasticity to some extent reflects the inclusiveness of the policy changes in the region to
the region. In doing so, they have documented that the share of income flowing to top earners has often been stable – if not increasing – in many countries in the region since the 2000s (Flores _et al._ , 2020, Medeiros _et al._ , 2015). These results are often not contradictory with the observed compression in wage distribution documented previously – instead, they highlight the importance of correctly assessing other sources of income that are often not captured in surveys, particularly income from capital. Accounting for the full national income when measuring inequality often leads to much higher top income shares, leading De Rosa _et al._ (2022) to suggest that ”either the region is more unequal than previously thought or it is not as rich”.

In this paper, we contribute to this debate by creating Distributional National Accounts (DINA) for Honduras, a lower-middle income country in Central America. We household data with administrative tax records and macroeco- complement survey nomic aggregates to provide measures of top income shares in the country for the 2003-2019 period. Our key findings are that the shares of pre-tax income flowing to top earners in Honduras are among the highest in the world, and they have been stable over the last two decades. The highest 1% of earners in 2019 received approximately 1
10.9|18.5|27.5|-0.37<br>|
|Eighth Decile Products|246,657|39.1|11.7|27.1|31.9<br>|-0.77<br>|
|Ninth Decile Products|142,627|38.4|9.0|4.0<br>|5.4<br>|-2.15<br>|
|Tenth Decile Products|287,531|40.7|10.9|8.2|28.2|-21.65|

*Includes Brazil's recent restrictions on automobile imports and domestic<sup>automobile assembly regulations.</sup>

Source: Computed from SMART and the WTO Integrated Data<sup>Base.</sup>
Figure 11: Utilization rate losses from infrastructure disruptions, by type of infrastructure at country level <!-- Start of picture text --> a. Electricity infrastructure b. Water infrastructure c. Transport infrastructure<br>2 2 2<br>1.8 1.8 1.8<br>1.6 1.6 1.6<br>1.4 1.4 1.4<br>1.2 1.2 1.2<br>1 1 1<br>0.8 0.8 0.8<br>0.6 0.6 0.6<br>0.4 0.4 0.4<br>0.2 0.2 0.2<br>0 0 0<br>0 20000 40000 0 10000 20000 30000 40000 0 10000 20000 30000 40000<br>GDP per capita GDP per capita GDP per capita<br>Electricity infrastructure Water infrastructure Transport infrastructure<br>infrastructure disruptions<br>Percentage of utilization rate loss due to<br><!-- End of picture text --> _Note:_ Data points represent 118 countries.

Figure 12 and Figure 13 show the most affected countries overall and by infrastructure type, respectively. In some countries, unreliable electricity infrastructure causes large fractions of industry capacity to remain unused, often significantly more so than transportation problems. At the same time, firms in many countries are barely affected by power outages as 46% of countries experience utilization losses of less than 0.05% due to electricity. Other than in the case of transportation disruptions, many countries appear to be successful in eliminating their firms’ losses due to unreliable electricity.

Unreliable water infrastructure has a small affect in most countries. Whereas some countries’ firms suffer significant losses from disrupted water infrastructure, most countries appear to be affected only moderately. This might indicate countries’ success in eliminating outages as well as firms’ lower reliance on water infrastructure
41.9|-1.4||
|Q2 in mobility drop|-45.7|-44.3|1.4||
|Q3 in mobility drop|-40.7|-44.6|-4.0|*|
|Q4 in mobility drop|-46.7|-50.0|-3.2|*|

* indicates statistical significance of the gender difference at the 5% level. Averages over the full sample. Full set of results available in the appendix. The aggregate unconditional average is the estimate for _β_ in Equation 1. The other predictions exploit the estimates for Equation 2. The aggregate conditional average is the predicted value of the outcome if every business in the sample were led by a man/woman. The predicted averages in the following rows condition on other characteristics of the business in addition to the gender of the owner or manager.

# **5.4 Financial risks**

The COVID-19 shock was associated with a sharp decline in firm liquidity as many firms reported severe difficulties in meeting their financial obligations (Apedo-Amah et al., 2020). The BPS and WBES surveys measure liquidity constraints and financial risks among firms using two questions that seek to capture the ability to meet their current financial obligations (e.g. rents, wages, interest payments, etc.) and the likelihood to be in or soon fall into arrears.<sup>15</sup> At the time of the interview, women-led businesses reported on average less cash available to cover their costs (Table 5). Men-led businesses report on average the predicted equivalent of 70 days of cash available to cover costs, whereas women-led businesses report only 61 days, a statistically significant gap. When controlling for size and sector using the conditional model, the difference remains statistically significant, indicating that most of the difference in available liquidity between men-led and women-led businesses does

> 15The two questions read _(i_ )As of today,
grants and guarantees extended by national agencies of up to 134 bilateral creditor countries and by 56 different international organizations (see Appendix III for a full list). Whenever available we collected data on the creditor and the borrower, both commitment and disbursement amounts, the purpose of the loan as well as the interest rates and repayment terms. In other cases, our data is more aggregate. At the minimum, however, our database contains creditor and borrower country as well as commitment amounts at the annual level allowing us to identify source and direction of official lending and thus the bilateral patterns of international official flows. We now sketch the main sources used in constructing the database.

_International treaty series:_ Our main source for official lending during the 19<sup>th</sup> century and leading up to World War II are international treaty collections. In doing so, we make use of the fact that the conduct of foreign policy became increasingly formalized in the late 18<sup>th</sup> century, so that international loans between two states were now codified in inter-governmental treaties (Keene 2012). Our sources show that, over the course of the 150 years from 1790 to World War II, sovereigns negotiated thousands of bilateral treaties, in which they regulated their political and commercial interactions. To identify bilateral lending, we systematically search the available treaty collections and related databases for bilateral financial agreements that arrange the extension of loans and grants or that extend guarantees on privately issued international loans or bonds.

_National budget accounts:_ For the main bilateral creditor countries, we supplement the information obtained from international treaties with national budget accounts and plans. In particular, we comb through the national budgets of the United Kingdom starting in 1790, France from 1790, Germany from 1870 and the US starting with its first official, foreign loan in 1917. In addition to providing an important consistency check, these sources allow us to capture within-empire (country) transactions, e.g. lending to colonies, for which