rafmacalaba/data-use-mentions-extended huggingface dataset:
Accurate measurement of employment and the labor force is critical for guiding well targeted and timely policy making. Indicators like labor force participation, employment, and unemployment measure the degree to which segments of a population engage in economic activities. These are widely used to design and implement programs central to economic development – such as programs that tackle youth unemployment or programs that aim to boost access to markets for small producers. In addition, because a significant component of household income is from employment, inaccurate measurements of employment can lead to mismeasurement of household income. Biased or incomplete identification of these critical indicators can lead to misrepresentations of growth, social development, and poverty.

At the same time, the distinction between employment and economic activity on one hand and other tasks that can increase the well-being of households on the other hand is not always obvious. Small-scale agricultural production for own consumption is a clear example of where the distinction between the two may fail – these are activities that can result in production which may or may not be used for economic exchange. On top of these conceptual difficulties in economic measurement, there are also contextspecific definitions of which activities are considered work and who can be a worker. Through these definitions, gender roles and the allocation of household tasks across genders can have a significant impact on the robustness of official employment statistics.

The underreporting of women’s economic contributions and work has long been recognized as impacting labor force statistics and national accounts estimates (Boserup 1970, Anker 1983, Beneria 1981, Dixon 1982, Donahoe 1999, Mata-Greenwood 2000). Researchers argue that the very definition of employment excludes some activities that are typically performed by women, including some types of informal activities and domestic work. However, even _within_ activities that are included in the definition of employment, women’s participation is undercounted because of how the concept of ‘employment’ is operationalized in surveys and how respondents understand it. This has resulted in a systematic underestimation of women’s involvement in agricultural work, especially self-employment and seasonal or part-time
|19|1|
|Burkina Faso|1|11|1|
|Cape Verde|10|5|23|
|Central African Republic|0|2|1|
|Côte d'Ivoire|0|23|5|
|Gambia|0|7|7|
|Ghana|5|19|9|
|Guinea|0|8|2|
|Guinea-Bissau|0|2|1|
|Liberia|0|8|1|
|Mali|0|16|1|
|Niger|0|8|1|
|Nigeria|22|31|19|
|Senegal|4|24|18|
|Sierra Leone|0|8|2|
|Togo|0|11|1|
|Source: Derived from Bofinge|r 2009.|||

connected to the francophone hubs or to any of the other surrounding countries in the region.

Beyond basic connectivity, it is important to evaluate the convenience and velocity of air travel. For some countries, most of the flights that originate from within the country are direct. To a large extent, flights that orginate from Ghana and Nigeria are direct, because Accra and Lagos are hubs for air travel in West Africa. Senegal is striking in that of the 80 flights that depart, only 59 are direct. On average, flights from Burkina Faso, Guinea Bissau, and Togo cover fewer kilometers per hour than other parts of the subregion (table 2.11).

37
1.609**<br>h 1|-0.903<br>t ll *|-0.886*<br>* t th 5|-1.209**<br>t|-0.719<br>l d *|

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. “Recessions: Baseline definition” are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years of output growth that is both negative and one standard deviation below the long-term average (as in Huidrom, Kose, and Ohnsorge 2016). “Mild Recessions: Alternative definition” are defined as years of negative output growth only, regardless of the depth of the output decline. Sample includes unbalanced panel of 32 advanced economies and 79 EMDEs for 1981-2020.

65
# **4.3 Sales price regressions**

Results from land sales price regressions are in table 7 with columns 1 and 2 reporting results for the close to 175,000 and 100,000 sales of commercial and private land, respectively and the remainder focusing on commercial land purchases by legal entities the vast majority of which took place in 2024: regressions for some 26,000 sales with only registry information in columns 3 and 4 and some 8,500 and 2,000 with tenant without and with registry information in columns 5 and 6.<sup>20</sup> To interpret results from the land sales price regressions, note that the sale price is just the present value of the stream of rental payments, discounted at the market interest rate. If markets function well, we expect estimated coefficients from the land sale and rental price equations to be identical. Any differences thus point towards potential frictions that would be worth further investigation.

_Sale market competition depresses prices:_ With an estimated elasticity of -1.21 % for commercial and -4.3 % for personal land, higher levels of competition seem to be associated with lower rather than higher sales prices. This points to constraints on the demand (financial market frictions) or supply side (prices too low or an expectation of higher land prices in the future) that would be worth further investigation.

_Legal entities pay a premium, especially for personal land:_ While the 30% premium paid by legal entities for commercial land is in line with that for commercial land, a premium of 60% for personal land supports the notion that some of this land may be structurally different from commercial land, most likely as it is more suitable for non-agricultural uses. Use of satellite imagery to assess post-transfer uses of such lands is one way to explore if a possibility to ‘informally’ convert such land to residential or industrial use may have partly driven such a large premium.

_Informality results in a discount for commercial land:_ For
implications for developing economies. Our findings highlight the importance of institutional factors and suggest that developing countries should focus on improving the institutional environment in addition to providing active public and credit markets and growth opportunities to attract more buyout capital relative to other traditional forms of investments.

# **4.3 Where are reforms more effective?**

Our main results have shown that investor protection and contract enforcement reforms are associated with more buyout investments on average, but it may very well be the case that the impact of these reforms is different across the cross-section of countries. To understand if and where these reforms are more effective, we study two such dimensions across which the impact of reforms might be different: legal environment and human capital.

While reforms are more likely needed in countries with a weaker regulatory environment to start with and might be more effective in such circumstances, well-functioning institutions and a strong legal system could potentially make the implementation of investor and contract reforms more effective in attracting more buyout capital. We test this by adding interactions of the reform dummies with various measures of legal environment to our estimations in Table 5. Results are presented in Table 8. We use scores on rule of law and regulatory quality from World Bank’s Governance Indicators and the corruption index from Transparency International to proxy for the strength of overall governance in a country. Columns 1, 3, and 5 show that the coefficients on the interaction of _Investor Reform_ dummy with the governance variables are all positive and statistically significant, suggesting that the investor protection reforms are indeed more effective in attracting more buyout capital in countries with a strong governance environment in place. The coefficients on the interactions with the _Contract Reform_ dummy in Columns 2, 4, and 6 are 20
To summarize the sectoral breakdown of the effectiveness of government policy, our analysis confirms the intuition that government support has helped manufacturers of textiles and garments (in El Salvador) and hotels and restaurants (in Georgia). Less intuitively, food manufacturers in El Salvador appear to have benefited from government support through avoiding changes in permanent full-time workforce, though the same effect in Georgia is opposite but not statistically significant (perhaps due to the composition of food manufacturers within each country, with supply chains leading towards hotels or restaurants as opposed to grocery stores). We also find evidence of counter-productive effects of government policy in some sectors. In particular, government support was found counter-productive in Georgia for food retailers and pharmacies across two of our four outcome variables. Overall, the outcome variables on the incidence of decreased total hours of operations and expectations of falling in arrears offer a number of counterintuitive findings. The former is likely attributed to the conflict between perceived health safety, which likely forced firms to reduce hours of operations, and the business objective of staying open to keep earning revenue. As for the expectations of falling in arrears, we conclude that this variable is rather noisy in general, perhaps pointing to an unprecedented uncertainty that firms were facing during the pandemic, complicating the formation of logical and grounded expectations about the near future.

# **7. Summary and Conclusions**

In this paper, we used firm-level data collected before and during the COVID-19 pandemic (ES and COV-ES) in El Salvador and Georgia to estimate the effectiveness of government support to the private sector in terms of firms’ workforce, operations, and expectations. While ES and COVES data cover 44 countries, we focus on just two because, in most of the other countries, the government support was available only to the firms whose experience during the pandemic was at or worse than the set thresholds, such as a drop in revenue or workforce, etc. Such selection criteria, coupled with the fact that this information is not normally contained in data, make
and livelihoods. Given these are not exact measures of exposure at the household level, our estimates are still technically ITT. However, we argue that for the short distance buffers based on which exposure is defined, it is unlikely that these ITT estimates differ much from ATE.

Despite these alternative probing specifications our analysis is constrained by some remaining shortcomings, which merit discussion. First, while the pre-conflict (pre-pandemic) sample of the LSMS-ISA in Ethiopia is randomly selected from urban and rural households in each district, the follow-up phone surveys are prone to systematic non-response and attrition. To account for these systematic non-responses in the phone survey, we construct and employ sampling weights as discussed in Section 2. These sampling weights can help to recover appropriate and representative statistics if the observable characteristics we use to construct our weights sufficiently capture these systematic non-responses and attrition (Wooldridge, 2007; Korinek et al., 2007). To the extent that they fail to control for this bias, our results may be taken as a lower bound of the actual impacts of the conflict. A final issue is the potential uncertainty related to the impact of COVID-19 related economic restrictions, which may interact with the effects of the war. Most mobility restrictions and pandemic-related policies in Ethiopia had been relaxed long before the outbreak of the war. In the worst case, our maintained assumption is that these are additive effects which may be captured by the time dummies or household fixed effects. However, it is possible that violent conflict largely substitutes for economic restrictions. If this is the case, then we might worry that economic or mobility restrictions are only imposed (or enforced) in areas not affected by the war. In such a case, our estimates are also lower bound estimates of the actual effects of the conflict.

# **5. Results and Discussion**

In this section, we present three sets of results based on the approaches outlined in Section 4: DID, disaggregated DID and ACLED-based results. In all our estimations, we apply the sampling
There are two caveats. Because it was difficult to find valid instrumental variables to address the genetic correlations (ability and preference) between parents and their offspring, the study does not distinguish the effects of nature and nurture. We limit our analysis to investigating the correlation between the educational attainment of parents and children without implying any causality. Second, for all countries in our sample, we rely on single cross-sectional surveys and study intergenerational mobility among five-year age cohorts. Hence, we cannot assess the extent of measurement error, if any, in the education variable of both parents and their children. Future research might therefore involve examining the importance of these elements to document the importance of the recent education policy changes in the region in promoting social mobility.

28
|2018|0.38|0.41|0.47|0.44|0.33|0.48|0.55|0.52|
|2019|0.39|0.42|0.45|0.46|0.38|0.49|0.54|0.51|
|2020|0.35|0.45|0.45|0.46|0.29|0.44|0.54|0.50|
|2021|0.35|0.45|0.41|0.45|0.31|0.42|0.54|0.50|

_Notes:_ Based on data from EH 2011-2021. Estimation using a rescaled score from the first principal component of the PCA. Following INE’s methodology, the estimation for the Beni and Pando regions was done together.

Table 11 explores the evolution of job quality across different types of employment 22
congestion as measured by Waze to predict vehicles on the road during the months when we do not have data for some of the intersections.<sup>2</sup> In a context like Nairobi, where there is not a good measure of vehicles on the road or vehicle miles traveled, this high-frequency data provides one of the best measures for how the number of vehicles traveling throughout the day might be changing during this period.<sup>3</sup> The predicted vehicle counts after the curfew is implemented across the different intersections demonstrates very similar magnitudes of change. This makes us more confident that the changes we measure in these intersections are likely representative of vehicles on the road more broadly in the city. We generate an index for each intersection for vehicle counts using vehicle counts in the pre-curfew period of 2020 by day of week as the baseline. We use an average of this index across intersections as a proxy for the change in total vehicle counts for the city.

To measure how the curfew may have affected drivers’ behaviors taking into account the overall decrease in mobility, we create a proxy of crash rates (crashes per car density) estimated as total road traffic crashes divided by the index of vehicle counts in 6 principal intersections in Nairobi described previously. This allows us to assess if there were changes in the probability of a crash due to the curfew in a context of lower mobility.

To measure speed, we query real-time travel times from Google Maps. We query travel times along segments of major corridors in Nairobi, including Mombasa Road, Uhuru Highway and Waiyaki Way, that we selected due to their importance for travel within Nairobi. We obtain information on the time it takes to travel from the start to the end of the segment using a vehicle given current traffic conditions. The speed is calculated by dividing the distance of the segment by the estimated travel time. We query this information every 30 minutes. We verify these measures using speed information from Uber Movement data. Uber provides speed data
the open model for regulating data. In contrast, there is no country with the limited model that is assessed as having a free internet.<sup>19</sup> Figure 10 concludes this descriptive section by looking at the countries’ domestic institutional quality. Given that economic access to the internet is related to a countries’ independence of regulatory bodies, domestic regulatory institutions in a wider sense be an important factor for trade in digital services. The proxy for this analysis is the indicator from the World Bank Governance Indicators which measures the country’s regulatory quality. Figure 10 shows that countries with greater regulatory quality exhibit higher digital services exports. Countries sharing the conditional model exhibit on average a higher score than countries following the other models, even after excluding EU member states.

Figure 10: Digital services trade, data models, and domestic institutions (2015) <!-- Start of picture text --> Digital Services Trade and data models<br>By regulatory quality<br>100<br>10<br>1<br>.1<br>-1 0 1 2<br>Regulatory quality<br>Open Conditional Limited<br>Per capita exports in 1000 Mln. USD (log)<br><!-- End of picture text --> Source: Authors’ calculations using TiVA trade data, World Bank WDI and Governance Indicators. Countries are categorized on the basis of the data model regarding the cross-border data transfers.

# **4. Estimation using the Gravity Model**

This section presents the baseline gravity model used to estimate econometrically whether sharing the same data model is correlated with higher or lower digital services trade.<sup>20</sup> In doing so, the

> 19 The midrange of a partly free Internet is taken up by more or less an equal number of countries having a US or EU model. The Freedom House assesses the openness of the Internet for 65 countries, of which 6 are EU member
event, Real<br><!-- End of picture text --> Source: Centre for the Study of Civil War at the Peace Research Institute Oslo (PRIO), CRED/OFDA International Disaster Database, Huidrom et al (2019), International Energy Agency, World Bank. A. Slowdowns of at least 0.1 percentage point in annual GDP growth. Data for 36 advanced economies and 146 EMDEs.

B. Median cumulative impulse response of EMDE and global GDP growth after one year to a 1 percentage point decline in U.S. and euro area GDP growth. Based on vector autoregression of world GDP, output growth in the source country of the shock, the U.S. 10-year sovereign bond yield plus JP Morgan’s EMBI index, output in EMDEs excluding China, and oil price as an exogenous variable. The “global” sample includes 22 advanced economies and 19 EMDEs for 1998Q1-2016Q2.

C. See Huidrom et al (2019) for details. Cumulative impulse responses of EMDE growth after one year in response to a 1 percentage point decline in growth in origin of shock.
in higher productivity sectors such as manufacturing instead of agriculture (MGI, 2018).

**Figure 5. Gender Parity Index (GPI) for wages/salaries by main industries**

|Trade<br>Transportation<br>Finance<br>Services||
|---|---|
|Agriculture<br>Mining<br>Industry<br>Electricity, water, and gas<br>Construction||
|0<br>0.2 0.4 0.6 0.8<br>1|1.2|
|Source: Sakernas, 2017.||

In the civil service, women comprise about 42 percent of civil service employees across 34 ministries. Echelon positions—the system by which career progression is measured, and the level of which indicates an individual’s position of hierarchy in the civil service—comprise less than 7 percent and, of those positions, women held just 26 percent in 2016. Through interviews, it was learned that women are promoted less often and that women seek fewer opportunities for promotion. Women achieve lower scores in bidding processes for promotions and are less able to participate in training for higher echelon positions due to time constraints and responsibilities at home. Several women who were interviewed noted that there is a lack of adequate childcare facilities at district, provincial and ministry offices. Women and men both noted that some positions are targeted specifically for men and that men are seen as better leaders.

The trend is no better in the legislative and judicative bodies. Law No. 10/2008 on General Elections, Article 8/1d, states that the list of parliament member candidates proposed to the General Election Committee should consist of at least 30 percent women. However, in the last legislative election, the percentage of women selected in the national parliament was still less than 20 percent. Since the members of the legislative are elected through a <u>general election, the female candidates were not chosen because they did not
**Table 3.  Empowerment Indicators, Honduras Community Based Education Project**

|DOMAIN|SUB-DOMAIN|ASSET AND OPPORTUNITY STRUCTURE INDICATORS<sup>34</sup>|DEGREE<br>Federal|S OF EMPOWERMEN<br>Intermediary|T INDICATORS<br>Local|
|---|---|---|---|---|---|
|STATE|Service Delivery|•<br>Percentage of schools councils that are informed of the requirement<br>to acquire legal status to open up a bank account<br>•<br>Percentage of councils that possess an operational manual that lists<br>their responsibilities<br>•<br>Percentage of councils that have opened a bank account to receive<br>transfers from the government<br>•<br>Percentage of councils that file (1) bank statements, (2) receipts of<br>purchases and payments, (3) receipts of transfers<br>•<br>Percentage of councils that have received training on budgetary<br>matters<br>•<br>Duration of the training<br>•<br>Levels of perceived usefulness of the training||Percentage of school<br>councils that oversee<br>their budgets||
|||•<br>Legislation grants school councils the right to oversee their budgets<br>•<br>Provision is made to inform councils about their budget<br>responsibilities<br>•<br>Provision is made to train councils about budget responsibilities<br>•<br>Extent to which other actors (school principal, mayor, council<br>member, PROHECO district or departmental officer) intervene in<br>budget oversight||||

> 34 Asset indicators are listed in the shaded boxes, opportunity structure indicators in the non-shaded ones.

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- Development Bank, Abidjan, Côte d’Ivoire. https://www.afdb.org/fr/documents/document/working <u>paper-56-a-statistical-analysis-of-determinants-of-project-success-examples-from-the-africandevelopment-bank-9020</u>

- Odokonyero, T., Marty, R., Muhumuza, T., Ijjo, A. T., & Moses, G. O. (2018). The impact of aid on health outcomes in Uganda. _Health Economics_ , _27_ (4), 733–745. <u>https://doi.org/10.1002/hec.3632</u>

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Riddell, A., & Niño-Zarazúa, M. (2016). The effectiveness of foreign aid to education: What can be learned? _International Journal of Educational Development_ , _48_ , 23–36. <u>https://doi.org/10.1016/j.ijedudev.2015.11.013</u>

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Smets, L., Knack, S., & Molenaers, N. (2013). Political ideology, quality at entry and the success of economic reform programs. _The Review of International Organizations_ , _8_ (4), 447–476. <u>https://doi.org/10.1007/s11558-013-9164-2</u>

-
||
|Trade to Advanced Countries|..|..|..|..|..|..|..|..|..|-0.0702*|..|-0.0925*|
|_(% GDP, exports and imports)_||||||||||(0.0380)||(0.0483)|
|Trade to Developing Countries<br>|..|..|..|..|..|..|..|..|..|-0.0737<br>|..|-0.0960<br>|
|Outside the region_(% GDP)_<br>Intra-regional Trade|..|..|..|..|..|..|..|..|..|(0.120)<br>-0.0800|..|(0.178)<br>-0.113|
|<br>_(% GDP, exports and imports)_||||||||||(0.0723)||(0.0997)|
|_International Finance_|||||||||||||
|Gross Capital Inflows<br>..|..|..|..|..|-0.158|-0.163|-0.183*|-0.191*|-0.0512|-0.138|..|..|
|_(% GDP)_|||||(0.173)|(0.126)|(0.104)|(0.114)|(0.134)|(0.0980)|||
|Financial Openness Index|-0.410|0