rafmacalaba/data-use-mentions-extended huggingface dataset:
1993–2011, in %)**

<!-- Start of picture text --> 35 70 100<br>30<br>65<br>25 80<br>60<br>20<br>55 60<br>15<br>10 50<br>40<br>5<br>45<br>0 20<br>40<br>-5<br>-10 35 0<br>01/93 01/96 01/99 01/02 01/05 01/08 01/11<br>-15 30<br>I/93 I/96 I/99 I/02 I/05 I/08 Non-financial corporates Households<br>Tradesmen Other financials<br>Credit growth %NPL Credit-to-GDP (rhs)<br>Government<br>Source: CNB<br>Note: Credit growth is year-over-year increase in total bank credit, Source: CNB<br>% NPL is the share of nonperforming loans on total bank credit.<br>Data from the beginning of 1990s are based on authors' estimates.<br><!-- End of picture text --> Given the relatively high exposure of the banking sector to the corporate sector associated with legacy loans, loans for financing inventory needs, and new loans associated with privatization, the credit-to-GDP ratio of the Czech banking sector was rather high at the early stage of transition—roughly 65 percent in 1993 (see Chart 1). In addition, nearly 90 percent of loans in credit portfolios were to the nonfinancial corporate sector. The share of households in the overall credit system stayed below 10 percent throughout the 1990s (Chart 2).

The deep banking crisis that hit the Czech economy from 1997 to 1999 was caused by a mix of flaws in the financial system and suboptimal steps in macroeconomic policies. (See, for example, Frait and Tamarappoo 1998.) The share of nonperforming loans exceeded
instruments in future pandemics and crises that are inherently costly as well as ineffective.

In this study, we shed some light on the question of whether curfews affect road safety. This is important because the cost of road safety is measured in people’s lives just as is done in pandemics. The context we use is Nairobi, a capital in a low- and middle-income (LMIC) country with high rates of car crashes, and where we have made a multiyear investment in data infrastructure for urban management that turned a data-poor into a relatively data-rich environment.

A small pre-COVID literature looks at curfews used in various contexts to curb night driving by inexperienced drivers. This is effective in reducing crashes (Williams & Preusser, 1997). Even when inexperienced drivers fully substitute night with day driving, crashes are reduced (Bolduc et al., 2013). Here we investigate what happens when these restrictions are extended to all drivers. The question we ask is whether the reduction of cars on the road mechanically decreases the number of car crashes, or whether the curfew displaces driving, increases speeds and has a rebound effect on car crashes.

In our context, the dusk-to-dawn curfew was implemented in March 2020 purportedly to prevent the spread of COVID-19. We use a double-difference strategy, harnessing detailed high-frequency data on road traffic crashes, to look at crashes before and after the curfew implementation and relative to the same period a year earlier. In so doing, 2
<mark>rights of both borrowers and lenders to facilitate lending. T</mark> he other proxy for the quality of financial institutions we use is a dummy variable equal to 1 if the country has credit bureaus and 0 otherwise. Data source for the variable is Doing Business, World Bank. <mark>Higher values of both the proxy measures imply better financial institutions to facilitate lending and borrowing. Thus, the prediction is that the interaction term between our corruption indicators and the two proxies for the quality of financial institutions is negative. We confirm that the results for these interaction terms are robust to controls for interaction terms between corruption and several other elements of the business environment such as regulation, human capital availability, rule of law, etc. They are also robust to control for the interaction term between corruption and GDP per capita, and between corruption and firm-size.</mark> <mark>The second testable hypothesis is a falsification test. That is, as pointed out by Blackburn and Sarmah (2006), red tape and other elements of the business environment raise the cost of conducting business, but they do not affect financial market outcomes. Thus, there is no evident reason why the impact on the likelihood of being financially constrained of factors such as the level of red tape and physical infrastructure availability should be less positive (or more negative) in countries with better financial institutions. However, if our corruption variable is a proxy for the broader business environment, then we should find similar effects of regulation and physical infrastructure as we find for corruption. We test for this formally using two separate measures of red tape and power outages (hours) experienced by the firms.</mark>

# **3. Base regression results**

# 3.1 _Incidence of petty corruption_

19
|**Prob.**|
|---|---|---|---|---|
|C|62.39024|0.722912|86.30404|0.0000|
|AGE|0.410145|0.008974|45.70317|0.0000|
|LOG(PREMIUM)|1.618070|0.073313|22.07059|0.0000|
|GUARANTEE|-0.134448|0.008383|-16.03824|0.0000|
|DEFERMENT|0.016582|0.007399|2.241063|0.0251|
|Male|1.345882|0.206458|6.518928|0.0000|
|Female|4.023704|0.089566|44.92436|0.0000|
|2002|10.66352|0.149209|71.46677|0.0000|
|2003|5.699579|0.152080|37.47739|0.0000|
|2004|8.253581|0.150549|54.82318|0.0000|
|2005|6.507061|0.156551|41.56508|0.0000|
|R-squared|0.639507|Mean dep|endent var|104.9609|
|Adjusted R-squared|0.638954|S.D. depe|ndent var|5.600486|
|S.E. of regression|3.365172|Akaike inf|o criterion|5.266519|
|Sum squared resid|73778.36|Schwarz|criterion|5.277954|
|Log likelihood|-17173.65|F-sta|tistic|1155.747|
|Durbin-Watson stat|1.754037|Prob(F-s|tatistic)|0.000000|

Source: Authors’ estimations on SVS data.
that labor responses operate through a mechanism of repeated exposure to violence and the threat of increasing exposure to violence in the future, rather than one-off idiosyncratic exposure. To further study this mechanism we leverage two contextual details about herder-related violence in Nigeria. First, herder-related violence spiked in 2018 and 2019—with the number of events increasing from less than 50 per quarter of the year to over 150, as shown in Figure 2. Second, between 2016 and 2018 three Nigerian states (i.e., Benue, Ekiti, and Taraba) enacted bans on open grazing, which raised tensions between farmers and herders and led to more frequent and more severe herder-related violence in these states.

To explore these channels, therefore, in this subsection, we present results where we differentiate effects across time (i.e., household surveyed in 2018-2019 vs. previous rounds) and space (i.e., household located in ban state vs. other states). Figure 7 reports shifts in both informal and agricultural work associated with exposure to herder-related violence differentiated across time and space. Panel (a) shows results on informal work where we use a binary indicator of whether an individual has worked on their own account or household enterprise within the last week. In the post-planting season, we find that

> 37For completeness, Figure A15 also presents effects on individual work for someone else outside of the home however, the results do not merit much attention and are left for reference to the Appendix.

18
standard distortionary taxes and investment adjustment costs.

We calibrate our model by using the available data from Kenya, which discovered an estimated 600 million barrels of oil in 2012 and is expected to start commercial production in the early 2020s. Although the proven reserves are relatively small in comparison to other oil producers, the revenues generated from them, which could reach 16 percent of GDP annually at peak, are likely to be significant for the Government of Kenya.

> 4The nominal side and New Keynesian features may be added if the model is used to study the short-run policy effects of fiscal management to resource revenue flows.

3
countries. Which EO products produce the largest or smallest coefficients varies from country to country suggesting the how well an EO product captures the truth about weather depends on the geography and climate of where it is looking.

Moving to mean daily temperature (see Figure 13), the figures are presented with the same column and row structure, but instead of having six ranking places, there are only three. This somewhat simplifies the view. Beginning with Ethiopia, in the weather only specification, ERA5 produces the largest coefficients while CPC and MERRA-2 which places for the smallest. When we include FEs, CPC and MERRA continue to switch places based on the dependent variable but ERA5 now always produces the smallest coefficients. Next, in Malawi, we see a clear and consistent ordering of outcomes: MERRA-2 always produces the largest coefficients while CPC and ERA5 switch regarding which one produces the smallest based on the dependant variable. Like with Nigeria and rainfall, one get similar results in Malawi regardless of what EO product was selected. Turning to Niger, MERRA-2 produces largest coefficients in the weather only specifications while ERA5 produces the smallest. This order changes once FEs are added to the specification. We next consider Nigeria, which does not present a consistent pattern in the weather only specification but produces a consistent ordering of EO products once FEs are added. For Tanzania, a similar but less consistent pattern exists. There is very little stability in ordering of coefficient size in the weather only specification but a fair amount of consistency in the specifications that include FEs, with MERRA-2 always producing the smallest coefficients. Finally, in Uganda, there exists a pattern very similar to Malawi. CPC always produces the largest coefficients while MERRA-2 and ERA5 swap places for the smallest coefficient. Overall, while there is must fewer changes in ordinality for EO temperature data compared to rainfall data within a country, there again is little consistency across country. In Ethiopia and Uganda, CPC tends to produce the largest coefficients while in Malawi and Nigeria it is MERRA-2 and in Niger and Tanzania
25 adoption from abroad. Data for both royalty and license fee payments and receipts are obtained from the World Bank World Development Indicators and are in current U.S. dollars.

# **Gross Foreign Direct Investment**

Gross foreign direct investment is the sum of the absolute values of inflows and outflows of foreign direct investment recorded in the balance of payments financial account. It includes equity capital, reinvestment of earnings, other long-term capital, and short-term capital. This indicator differs from the standard measure of foreign direct investment, which captures only inward investment. The indicator is calculated as a ratio to GDP in U.S. dollars and was obtained from the World Bank World Development Indicators.

# **Manufacturing Trade Share**

Two papers, namely Lee (1995) and Mazumdar (2001), show that imported machinery or capital goods tend to have a statistically significant positive effect on growth of per capita income across countries. They both interpreted their results as evidence of the positive effects of technological adoption from abroad on long-term economic growth. In light of the above, we attempt to capture the effects of imported capital goods by using the manufacturing trade share, which is defined as the ratio of the sum of manufacturing imports and exports to GDP. Note that manufacturing trade share also contains the amount of manufacturing exports, which we consider to be an indicator for international competitiveness.

Annual data for manufactures imports (as a share of merchandise imports), manufactures<sup>39</sup> exports (as a share of merchandise exports), merchandise imports40, merchandise exports41 and GDP were obtained from the World Bank World Development Indicators. The manufacturing trade share was computed as the ratio of the sum of manufactures imports and exports in current U.S. dollars to GDP in current U.S. dollars.

> 39 Manufactures comprise the commodities in SITC sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manufactured goods), excluding
The Impact of Flooding on Urban Transit and Accessibility — A Case Study of Kinshasa _Yiyi He, Stephan Thies, Paolo Avner, Jun Rentschler_

**<mark>Keywords</mark>** <mark>: flooding, multi-modal network model, travel disruption, infrastructure resilience, GTFS</mark>

**JEL Codes** : Q54, C63, R11, R14, R23

**Acknowledgments:** We would like to thank GoMetro for leading the public transport mapping, and in particular Bob Kabeya and Clayton Lane, as well as the enumerators that conducted these surveys. We thank Shohei Nakamura, Takaaki Masaki and Mervy Ever Viboudoulou Vilpoux for their help in accessing Kinshasa commuter and household surveys. We are grateful to Laurent Corroyer, Stephane Hallegatte and Kirsten Homman for their inputs and feedback throughout this project. We are also grateful to Catalina Ochoa for her review of an earlier draft of this paper and her thoughtful comments. This study was supported by the Global Facility for Disaster Risk Reduction and Recovery (GFDRR).
. Comparison with Administrative Data**

Thus far our results have established that the AAA performance measures are significantly larger and more precise (i.e. lower CV) than traditional estimates. However, are these estimates more accurate, i.e. more representative of the true values?

> 8 These data were collected by our Research Assistant from a sample of entrepreneurs in greater Accra in 2018; the data were not part of any evaluation. Table 1 does not include summary statistics for this sample.

22
0.741|0.438|0|1|0|1|4502|
||sexual violence he/she:|In case of rape, must seek<br>medical help|0.596|0.491|0|1|0|1|4502|
|||Must tell someone such as<br>teachers, friends, etc.|0.021|0.144|0|1|0|0|4502|

_Source_ : Authors’ analysis from student surveys.

_Note_ : Variables with range [1–4] are coded as: totally agree, almost agree, sort of disagree, and totally disagree. Variables with range [0–2] are coded as: 0 – not so sure, 1 – almost sure, 2 – totally sure.
There were also divergences in characteristics of dollar-denominated central government bonds issued across the two jurisdictions. Average spread at launch for bonds issued under U.K. law became distinctly higher after the crisis (Figure 5).

<!-- Start of picture text --> Figure 5: Average spread at launch across governing<br>600 Spread‐to‐ laws (dollar-denominated central government bonds)<br>benchmark<br>(bps) U.K. Law U.S. Law<br>500<br>400<br>300<br>200<br>100<br>0<br>Source: Authors’ calculations based on Dealogic data.<br><!-- End of picture text --> Following Ratha, De and Mohapatra (2011), we also convert the alphabetic ratings into numerical scores with higher scores indicating higher risks and worse ratings (see Table 4). This allows us to analyze average ratings. On average, bonds issued under U.K. law had weaker ratings and shorter tenors post-crisis (Figures 6 and 7). But average deal size was broadly comparable across the jurisdictions (Figure 8). This seems an indication that more established issuers had a tendency to flock to the United States. These observations provide a motivation for the inclusion of control variables, as there may be a number of factors associated with the spread difference. So, in addition to the maturity and size controls already used by some authors (Eichengreen and Mody 2004), we also included in our analysis bond ratings (as in Jaramillo and Tejada 2011) and added regional dummies to account for regional linkages with each jurisdiction. How far controlling for these differences would explain the observed spread difference is something we would empirically explore.

7
In recent years, social media, and especially Twitter, has emerged as a source of real-time information. Therefore, it is natural that in a context where there is a dearth of official data on a topic such as road safety, we turn to crowdsourcing through Twitter to produce more comprehensive information on road traffic crashes. As people move around cities that have been plagued by congestion, they have started to rely on social media and citizen reporting to help them avoid major traffic jams and decrease their commutes. Given the relationship between RTCs and congestion, platforms that crowdsource and broadcast traffic updates have the additional benefit of often reporting on RTCs. This makes it possible to use crowdsourced data to identify when and where crashes are occurring, which can be used to supplement and improve on existing official statistics.

While only around 1% of tweets contain geo-metadata, a growing literature has developed geoparses—or algorithms that extract location names from text. Tweets present a unique challenge to geoparses. State-of-the art geoparses, such as OpenCascais and Stanford Named Entity Recognition, rely on grammar rules to identify location mentions; however, tweets often do not follow grammatical capitalization rules and use clipped, ungrammatical sentences [1, 2]. New algorithms have been developed to geoparse tweets. This includes developing an algorithm that accounts for tweets that contain place references that are abbreviated, misspelled or highly localized [2]. Others develop gazetteers (location dictionaries) from sources such as Open Street Map and Geonames and search for names within the gazetteer in tweets, employing different approaches to account for misspellings or tweets using shortened names than are in gazetteers [3, 4, 5]. [5] provides a review of existing approaches.

Here we provide more information on the specific data that we used, how they were processed and the algorithms that were developed. These processes can then be implemented in different contexts where crowdsourced data on RTCs are available.

# **Twitter Data**

We generate crash data from social media. The main source of crowdsourced
11 observe no exchange rate changes. Across the inflation targeters the highest average exchange rate changes are reported in Brazil and, across non-targeters, in Argentina, which was subject to a currency crisis in the early part of the sample (2001 and 2002).

Some stylized facts can also be validated when we compare targeter and non-targeter group averages. For instance, inflation targeters show lower average inflation (3.97%) relative to the noninflation targeters (6.27%). Given this lower inflation, the group of targeters also present lower nominal interest rates (6.41%) than their counterparts (7.15%). Inflation targeters also experience lower economic growth (3.58%) than non-targeters (5.15%). Finally, ITers observe significant lower exchange rate variability (0.28%) than non-inflation targeters (0.73%). This last property makes our sample particularly different from earlier ones, such as Aizenman et al. (2011), who collect quarterly data from 1989 to 2006 for 16 countries (11 inflation targeters and 5 non-inflation targeters) and report average changes in real exchange rates of 2.50% for inflation targeters and -0.49% for noninflation targeters, which yields a close to 3% real exchange rate depreciation for targeters compared to non-targeters. In contrast, in our sample from 2000 to 2015, countries that follow IT practices have their currencies fluctuating by much less when compared to non-IT adopters.<sup>2</sup>

# **3.1 The model**

We employ a panel data approach that allows us to consider an aggregate specification for all countries in the sample. Following Aizenman, Hutchison, and Noy (2011), who run Taylor rule-type

> 2 In Aizenman et al. (2011), real GDP growth was relatively close across samples: 1.11% versus 1%. However, their figures were similar to ours on monetary policy: inflation targeters show lower average inflation
such as the enactment of a competition law, the establishment of competition and consumer protection authorities, or the establishment of modern dispute resolution mechanisms (El-Haddad 2023).<sup>2</sup> These took place only decades later around 2005. This hybrid liberalization model supported the growth of crony capitalism in Egypt which further restricted decent employment opportunities for the masses (El-Haddad 2020). As a result, the informal sector emerged as a ‘survival sector’ in the country (Tansel and Ozdemir 2019; El-Haddad and Gadallah 2021). But with its growth as a survival sector, the dominance of the male figure as a bread earner (Hoodfar 1997; El-Feki et al. 2017), traditional stereotyping and the constraints women face in joining this sector (Assaad 2005), the informal sector has also been gradually ‘de-feminized’ (Assaad 2002). Similar to its formal sector counterpart, the sector has been overemploying men. In fact, the formal sector has been employing 10 times as many women on average compared to its informal counterpart.<sup>3</sup> This is true in absolute terms, in relative terms, however, men are on average four times more likely to be informally employed as women (EIFBS). As a result the informal sector has further entrenched the gender employment gap in the country.<sup>4</sup>

# **3. EIFBS Survey Instrument and Stylized Facts**

_EIFBS Survey Instrument and Sampling Design_ We use unique and recently collected data from the self-designed 2020/21 Egyptian Industrial Firm Behavior Survey (EIFBS) of 2,383 Egyptian manufacturing firms. The data

> 2 See El-Haddad (2008) on dispute resolution in TC in Egypt

> 3 Calculated from EIFBS data.

> 4 Most recently there has been a very slight trend in increased female self-employment including in the household enterprise, especially in fashion and catering – also considered informal activities - triggered by increased access to digitization and online platforms. Data on these trends remain largely unaccounted
thus, their welfare.<sup>5</sup> Agriculture can help reduce poverty through two additional indirect channels. The first channel is the _“food price” pathway_ .<sup>6</sup> Increases in agricultural output supply can drive down food prices. Since many poor households are net food buyers and spend a substantial part of their income on food, reduced food prices improve their poverty and food security status (Darko et al., 2018). Second, improvements in agricultural output may indirectly affect households’ welfare through the _“non-farm sector” pathway_ . Growth in agricultural productivity provides a significant proportion of the raw materials used in Timor-Leste’s non-farm sector. The increase in incomes resulting from a growth in agricultural productivity could raise the demand for goods and services produced in the non-farm sector. This escalating demand could in turn stimulate employment in the non-farm sector through forward and backward linkages, and eventually increase off-farm household incomes (Hanmer and Naschold 2000; Mellor 1999).

# **2. Data and Methodology**

The study uses nationally representative data from the 2007 and 2014 waves of the Timor-Leste Survey of Living Standards. The surveys, which spanned over a 12-month period of data collection, are very comprehensive and cover the following subjects: demographics, housing, access to facilities, durable goods, education, health, employment, social capital, and subjective well-being. In all, 4,477 and 5,916 households were covered in 2007 and 2014, respectively, of which 72 percent in 2007 and 52 percent in 2014 were agricultural households. The analyses in this paper focus on the agricultural households. Since only a sub-group of the population (agricultural households) is included in the analyses, the two cross-sectional surveys are therefore pooled to increase the sample size in hopes of improving the precision of the estimates.

To answer the question on the extent to which agricultural productivity affects poverty, the analyses are conducted using five measures of poverty: whether or not a household is