rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.2 Sorting Patterns: Where Do Women Work?**

Figures 1A-F plot kernel weighted polynomial bivariate regressions of the gender composition on firm-size, capital intensity, value-added per worker, wages and share of output exported, to document sorting patterns by gender. They reveal a very strong association between firm-size and the share of women in the workforce; small firms tend to be majority-male firms, while large firms tend to be majority female-firms. In part because they tend to be larger, firms that employ a larger proportion of women are also more likely to export. Yet, the share of women in the workforce is negatively correlated with capital intensity, value-added per worker and firm average wages, variables that are positively correlated with firm-size. Firms that use proportionately more blue-collar workers also employ proportionately more women (see figure 1F). The coexistence of these patterns points towards the importance of sorting by gender into different types of industries and firms.

Table 1 presents descriptive statistics by industry, ranking them on the basis of the share of women they employ. Industries which are highly labor-intensive pay relatively low wages and have a high export propensity, such as the tobacco and apparel industry, employ a lot of women. By contrast, capital intensive industries, such as transport and motor vehicles, basic machinery and equipment employ relatively few women. These patterns are by no means unique to Indonesia; very similar gender sorting patterns have been documented in a host of other countries (see e.g. World Bank forthcoming, Chapter 5 and the references therein). Possible explanations for these sorting patterns are that women have a comparative advantage in industries that require fine motor skills, women may be less likely to be involved in labor disputes which can be a particular concern in large firms, and that women may lack more favorable outside opportunities (relative to men).<sup>15</sup> Given the sorting of women into larger firms, we will present weighted regressions. Unweighted statistics reflect average outcomes for firms
level is correlated with both expectations and uncertainty as businesses in those countries hit by a larger crisis (as measured by the magnitude of the mobility drop) are significantly more pessimistic and have higher uncertainty (Figure 22).<sup>20</sup> Fourth, while country characteristics, proximity to the peak of the crisis, and sectoral characteristics all matter to explain expectations, these are much less important in explaining uncertainty which is largely explained by firm-level unobserved characteristics (Figure 23 and Figure 24). In other words, as shown by Figure 24, the variation in the uncertainty between country-size-subsector groups is smaller than the variation between firms within these groups.

Finally, expectations and uncertainty have an immediate connection with jobs and welfare. In fact, we find that those businesses that are pessimistic or have higher uncertainty are significantly more likely to have fired more workers (Figure 25).

# **7. Conclusions**

The results from the business pulse surveys draw a sobering picture on the impact of the shock. Firms have been severely affected by the COVID-19 pandemic, often through multiple shocks at the same time. Not only do firms report a significant drop in sales, but the financial situation is

> 18The reasons for these differences are due mainly to the fact that we implemented our survey over the phone and with many entrepreneurs that had varying levels of literacy, and we had more stringent needs in terms of simplification and time.

> 19The comparison with the United States is relative to expected sales growth ranging between -3% and 0% as reported by Atlanta FED Uncertainty Survey available here.

> 20To measure severity, countries in the dataset are sorted into quartiles based on the drop in mobility at the peak of the crisis.

12
.53|
|1998-99 Urban mean<br>1.69<br>1.71|1.47|1.46|

_Source_ : Authors’ analysis based on 1998-1999 India Time-Use Survey (TUS) and National Family Health Survey (NFHS) rounds 2, 3 and 4. _Note_ : Panel A is based on applying the equations in (Henry 2005) to heights and weights reported in NFHS. Panel B is based on using the matched activity factors from FAO (2001) and activities reported in the TUS (see Appendix Table S.2). Reported values are population statistics using survey sampling weights.

29
this information to the patient. In section 3, we explain how we operationalized these pricing treatments. The remainder of this section introduces the model and outlines the key testable predictions that identify patient- and doctor-driven demand. We leave the full model and formal analysis for Appendix A.

> 15Our home survey found that just 6 percent of individuals who declined to purchase at the clinic obtained an antimalarial from another source after leaving the clinic.

> 16Appendix B gives additional detail on the health worker survey.

12
>13</sup>

# **_3. Methodology_**

The first objective is to describe recent patterns of export entrepreneurship in LAC countries using the list of indicators defined in Table 1. Those indicators defined at the countryyear level are analyzed in Section 3 while indicators defined at the country-year-product level are analyzed in Section 4.<sup>14</sup>

> 11 Lederman, Rodriguez-Clare and Xu (2011) excluded the products that were probably exported by INTEL and Abbot Labs during 1997-2007, because these multinational firms began operations in 1998 and thus dominate any analysis of the contribution of export entrepreneurship. Since the story of these cases is relatively well known, our interest is to assess entrepreneurship among the rest of the firms in Costa Rica.

> 12 Indicators for Nicaragua in the _Exporter Dynamics Database_ are calculated excluding firms located in Export Processing Zones given the criterion of data quality used which is the match between the total exports calculated based on the exporter-level customs data and the total exports reported in WITS/COMTRADE.

> 13 For Ecuador in the period 2005-2007 only years 2006 and 2007 are available.

> 14 In addition to the indicators listed in Table 1, Section 4.1 mentions the average number of HS 6-digit products per type of firm (exporter, entrant, incumbent) and the average number of destinations per type of firm (exporter, entrant, incumbent).

8
majority of emergency calls made to Flare are for RTCs. Of the calls coming in, almost half come from good Samaritans who witness a situation requiring an ambulance. The other half of the calls are evenly split between calls coming from the police who show up on the scene of an emergency and calls coming from those who are part of the membership service that Flare offers.<sup>1</sup> Similarly to the data from the police, we aggregate data on the number of RTCs recorded by Flare per hour for January 1, 2019 to December 31, 2020.

We rely on four data sources to measure changes in vehicle density and speed. We use traffic jam information from the Waze Connected Cities Program (Waze, 2021). Waze provides information on the location, speed, and delay time of traffic jams; data is updated at 2 minute intervals. We compute the total traffic delay time due to traffic jams in Nairobi hourly. We use this delay time of traffic jams as a proxy for congestion on the roads.

We use data on number of vehicles on the road measured from from road sensors installed in six major intersections in Nairobi by the Kenya Urban Roads Authority. Data for four of the intersections is available for January and April 2020, for one of the intersections data is available for February and April in 2019 and 2020 and July 2020, and for one intersection data is available for January-April 2020. We use the relationship between vehicles on the road and

> 1Businesses can sign up through Flare’s membership service, rescue.co, to have their employees or customers receive access to Flare services free of charge. This service has been especially popular with ride sharing companies.

5
# **I. Introduction**

In recent years, the U.S. military has increasingly used development projects as a strategic weapon to fight ongoing counterinsurgency efforts in Afghanistan, Iraq, and other theaters. The approach is predicated on a hypothesis that such projects - which are commonly implemented by the domestic government and allied entities and deliver basic services and infrastructure - can improve economic outcomes, build support for the government, and ultimately reduce violence as sympathies for the insurgency wane. As evidenced by its prominence in the U.S. Army’s _Counterinsurgency Field Manual_ , the hypothesis now constitutes a major component of current U.S. counterinsurgency doctrine (U.S. Army / Marine Corps, 2006).

Despite the ongoing application of the strategy, there is limited empirical evidence on the effectiveness of development projects in countering insurgencies. In this paper, we use results from a large-scale randomized field experiment involving Afghanistan’s largest development program - the National Solidarity Program (NSP) - to test mechanisms by which development projects can potentially affect counterinsurgency outcomes. We find that villagers residing in communities which have received projects are more likely to hold positive perceptions of their economic situation and exhibit positive attitudes towards the government. We also find that the areas around villages which receive NSP become safer, although this effect is limited to regions with moderate levels of initial violence.

Between 1960 and 2010 more than half of the world’s countries were affected by civil conflict, 20 percent of which had been at war for at least ten years (Blattman and Miguel, 2010). Insurgencies are a subset of civil conflicts that are largely irregular, asymmetrically-fought, yet prolonged attempts by anti-government elements to overthrow the government or win autonomy for a region or territory (Iyengar and Monten, 2008).<sup>2</sup> Counterinsurgency, in turn, refers to all economic, political, and military steps taken by the government and allied forces to defeat the insurgency.

According to theories of civil conflict, the
to be transformed such that the total budget of the country within the six steps that correspond to a one year matches the government expenditure. We do this by simply assuming that the government distributes its budget equally within each simulation step in a year ( /6). BB<sup>�</sup> (tt) In the prospective analysis we assume that the yearly value of the government budget is the same always and it is equal to the average of the estimated values for the government expenditure between 2011 and 2019.

**Quality of law and monitoring:** We approximate the quality of law and monitoring using data from World Bank’s World Governance Indicators. The data on the quality of law comes from the rule of law variable and the quality of monitoring from the control of corruption variable. In the retrospective analysis we set their value to the ones observed in 2011, whereas in the prospective analysis we set

40
# **1. Introduction**

The business environment (BE) and entrepreneurship of developing and transition economies have received considerable attention from both researchers and policy makers over the decades (Berkowitz and DeJong, 2005; Dethier et al., 2010; Djankov et al., 2005; 2006; McMillan and Woodruff, 2002; World Bank, 2005; Xu, 2011). Indeed, creating a sound business climate is regarded as a key strategy for development, as it offers opportunities and incentives to entrepreneurs and firms to invest, create jobs, and expand business, consequently contributing to growth and poverty reduction (Stern, 2002).<sup>2</sup> To help improve BEs in the developing world, the World Bank has conducted firm surveys (i.e., the World Bank Enterprise Surveys, or WBES) to consult managers/owners about the obstacles of doing business in more than 135 economies since 2002.<sup>3</sup> The availability of cross-country firm-level data allows researchers to study the impact of BEs at the firm level. Numerous studies have been devoted to discussing how entrepreneur behaviors and firm performance are affected by a broad range of BE factors such as legal environments, property rights protection, infrastructure, access to finance, regulation, corruption, and crime rates.<sup>4</sup> Perhaps because most existing data such as the WBES surveys evaluate the BE at a specific year, few studies explore how a broad range of BE aspects and entrepreneurship have changed over time. As a result, little is known about how the BE has evolved, its likely impact over the decades, and how entrepreneurship and firm characteristics respond to such changes in the BE. Such a gap renders our understanding about the BE incomplete. For instance, do various BE elements differ in their speed of adjustment? If so, what is the economic and political logic behind such disparities? Once some BE elements evolve, do entrepreneurs and firms take advantage of such changes
# 3.2 DROUGHT

We use Historic Agricultural Drought Frequency data from FAO, which defines drought events based on the Agricultural Stress Index (ASI) (FAO, 2023). Unlike the other hazards used in this paper, this drought data does not follow a probabilistic modeling approach and thus is less amenable to modelling extreme values. The historical frequency of severe droughts is calculated using the entire 39-year time series, spanning from 1984 to 2022. The ASI is based on remote sensing vegetation (NDVI) and land surface temperature (BT4) data, combined with information on agricultural cropping cycles derived from historical data and a global crop mask. Specifically, the vegetation health index (VHI) is defined as a weighted combination of two anomaly indicators of vegetation and temperature, which are based on the deviations of the actual observation from the historical range of NDVI and BT4. The pixels with a VHI value below 35% over a growing season (up to 2 seasons) are considered as experiencing severe drought. The ASI then captures the percentage of crop or grassland pixels within each administrative unit (based on the Global Administrative Unit Layers by FAO) affected by severe drought.<sup>11</sup> FAO provides data with a spatial resolution of 30 arcseconds (approximately 1 km) depicting the frequency of severe drought in areas where 30 or 50 percent of the cropland or grassland is affected by severe drought (as defined by ASI) for two seasons. We restrict drought exposure to rural areas using the GHSL application of the Degree of Urbanisation methodology (stage I) recommended by the UN Statistical Commission for classification (GHS-SMOD – R2023A), available at a resolution of 1km (Schiavina, Melchiorri, and Pesaresi, 2023). We consider return periods ranging from 5 to 40 years (based only on historical frequency) and map rural areas where more than 30 and 50 percent of cropland or grassland were affected in any growing season.

# 3.3 HEAT

We use global extreme heat hazard data from Ridder et al
agreements with shipping lines now stipulate a 96% accuracy of information provided<sup>22</sup> . Failure to do so attracts sanctions. Enforcement of CTOC agreements has been critical for success.

The interesting thing about the CTOC is that staff can commit to these targets as well and at the moment about 5% of TPT staff have committed to a service level performance of 28 moves per hour. Further incentives include a commitment to specific customers to be afforded an agreed level of service, be it reduced, during periods of disruption such as labor strikes, etc. while the customer commits to clearing cargo from the port within agreed times. With the bigger customers agreements may even involve doing bulk runs at night.

In an environment where inefficiencies prevail, the private sector feeds from the public sector’s inefficiency, therefore creating a virtuous cycle of inefficiency and corruption on both sides. The basic issue here is really a need to establish rules of engagement and enforcement on both sides but the onus is on Transnet to enforce the rules while fulfilling its mandate of providing an efficient service to its customers. And so far, this arrangement seems to be working well.

TNPA also established port user forums for some of the key stakeholders, coordinated and chaired by TNPA itself. These have been divided into six clusters as follows: Automotive, Coal, Containers, Iron Ore, Liquid Bulk and Manganese. In the case of the DCT and Pier 1 there is a container terminal advisory board which meets on a monthly basis. These meetings provide TNPA and its customers an opportunity to discuss issues and try to find solutions together. While the process started off with both parties playing blame games, the partnership between TNPA and its customers appears to be working well as reflected by the recent Barloworld survey of 2010. At least, more than half of those in the automotive industry proclaim that logistics capacity of SA’s ports has improved in the last two years and they also think that the loading and docking capacity is now competitive
supply inclusive of the “distributional cost”. We estimated cost of supply of electricity to domestic consumers by taking the lower bound of the price range DISCOs paid while importing/purchasing the electricity (NIS 0.53) and adding a 20 percent margin to cover distributional costs, surcharges, and recoveries.<sup>55</sup> This results in an estimated average cost of supply of 0.636 NIS per kWh.<sup>56</sup> We then calculated the subsidy for each consumption slab by taking the difference between the cost of supply (0.636 NIS per kWh) and the tariff in that consumption slab. Table 4-2 describes the subsidies.

**Table 4-2 The estimated electricity subsidy per kWh ranges from 0.178 to -0.0371 in the West Bank**

**and Gaza**

Electricity subsidy <u>per kWh by consumption slab, West Bank and Gaza</u>

||**Quantity**<br>|**Subsidy**<br>|
|---|---|---|
|**Slab**|(kWh per<br>month)|(NIS per<br>kWh)|
|1|1-160|0.18|
|2|161-250|0.14|
|3|251-400|0.07|
|4|401-600|0.03|
|5|More than 600|-0.04|

Source: Authors’ estimates based on Prime Minister’s Office (2017)

# **We performed the following steps to simulate direct effects from the domestic electricity subsidy:**

- (i) Identified the amount of households’ expenditure on electricity based on the self-reported “electricity charges” in the PECS.

- (ii) Estimated the quantity of electricity consumed by dividing total expenditure on electricity by the tariff (which varies by consumption slab).

- (iii) Calculated the direct effect as
Sources: Essl et al. (2019); Haver Analytics; International Monetary Fund; Kose et al. (2017, 2021); Kose, Ohnsorge, and Sugawara (2021); World Bank.

A.B. A rapid debt accumulation episode is defined as a period during which the debt-to-GDP ratio rises from trough to peak by more than one (country-specific) ten-year rolling standard deviation. The trough-peak years are identified with the algorism in Harding and Pagan (2002). There are a total of 283 government debt booms and 291 private debt booms in 101 EMDEs over 1970-2020. Episodes associated with crises are those which experienced financial crises (banking, currency, and debt crises, as in Laeven and Valencia, 2020) during or within two years after the end of episodes. For definition of episodes and sample, see Kose et al. (2021) and Kose, Ohnsorge, and Sugawara (2021).

B. Medians of cumulative output and investment eight years after the beginning of rapid debt accumulation episodes (in year “t”). Ongoing episodes, as of 2020, are excluded. *** and ** denote that medians between episodes associated with crises and those with no crises are different with statistical difference at the 1 percent and 5 percent levels, respectively, based on Wilcoxon rank-sum tests.

C. Data are available for up to 24 LICs and 33 LAC countries. Nominal GDP weighted averages.

D. Based on a sample of 69 countries, by HIPC (Heavily Indebted Poor Countries) status, with available data as of June 30, 2021, as defined in a list of LIC DSAs by the International Monetary Fund.

E. Non-concessional external debt as a share of general government debt. Median of up to 118 EMDEs. Averages over the denoted periods on the horizontal axis.

F. Figure shows share of 17 LICs meeting minimum standards as defined by Debt Management Performance Assessments (DeMPA) in December 2018 (Essl et al. 2019).

31
OECD 2007). On the one hand, strict EPL could constrain the flow of workers into emerging high-productivity sectors and discourage technological change that is laborsaving. On the other hand, because of commitment signals and expected tenure effects, it could increase worker effort and incentives to invest in human capital; at the same time, it could motivate productivity-enhancing investments to compensate for any additional costs associated with job security rules.

The empirical work on the impacts of EPL is complicated by methodological issues, some of which were discussed in section 2. Measurement is particularly troublesome since job security rules cannot easily be reduced to a single number, like the minimum wage. There are two dominant approaches used in the field. One is to create an index where values reflect the strictness of EPL. The most widely used example of this approach is the OECD EPL index which assesses countries on a 6-point scale in terms of the strictness of its provisions for protecting permanent workers against individual dismissal, for collective dismissal requirements, and for the regulations governing temporary forms of employment.<sup>32</sup> A second approach more directly attempts to measure the monetary costs an employer can be expected to incur in complying with job security regulations. For example, Heckman and Pagés (2004) compute the expected average cost (present value) at the time of hiring to the firm for dismissing a regular

> 31 In fact, in an early paper, Lazear (1990) shows that, in perfectly competitive labor markets with no credit constraints, job security provisions should have no employment effects since contracts could be written to compensate for any associated costs.

> 32 Each of these has an index and they are combined into a composite index.  For more on the OECD measure, see www.oecd.org/employment/protection.