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Perotti, R. (2004). Public Investment: Another (different) Look. Mimeo, Università Bocconi.

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185 countries. The data are taken from Pennings (2020), who in turn takes the survey data (not projections) from the ILO series “Employment-to-population by sex and age (%) – Annual”, Age (Youth,Adults): 15-64, using the latest period available<sup>10</sup> or from the World Bank JOIN database, which focuses on low income and middle-income countries.<sup>11</sup> In most cases, the ILO data are the most recent. For three countries (Burundi, Sierra Leone and Togo) the basic GEGI as calculated by Equation 2 is slightly negative, and so in these cases I round up the basic GEGI to zero so it is between 0 and 100%.

As discussed in Pennings (2020), in a number of developing countries employment rates change dramatically over time. Most likely this is due to changes in the definition of employment and whether it includes or excludes own-use production workers (subsistence agriculture) — though this is rarely properly documented.<sup>12</sup> Definitional changes in employment are less of an issue for the GEGI than for other measures, as we are interested in the relative gap between male and female employment rather than its absolute magnitude.

# **2.4 Results – Basic GEGI**

The Basic Gender Employment Gap Index (GEGI) is shown in Figure 1, plotted against income per capita (log scale). On average the GEGI is close to 19%, indicating that long-run GDP per capita would be almost 20% higher if the gender employment gap was closed. The basic GEGIs are listed in Appendix Table 1 for all 185 countries.

> 8 The GEGI does not include other potential benefits of higher female employment, like slower population growth.

> 9 Note that because the World Bank Human Capital Index (HCI) used in the construction of the full GEGI is forward looking only, the full GEGI presented in Section 3 only has the second interpretation.
In combination, the results of the LEAP and LIAS tools can therefore provide a more detailed picture of drought and associated food insecurity in Ethiopia; this was proposed already early during the development of the LEAP tool in 2006.

# IV.4 Bottom-up Early Warning System

Recognizing the need to closely monitor droughts based on early warning data, the GoE has developed a bottom-up early warning framework through which monthly food security reports are produced. These are based on the use of checklists, as well as incident command systems. The bottom-up early warning system provides early warning data on a more continual, and less discrete, collection of early warning data, and could be used to replace the current seasonal needs assessment process. The bottom-up early warning systems could also be used to enable early action, as the gradual development of droughts is identifiable based on the collected data.

The successful deployment and maintenance of a bottom-up early warning system is likely to depend on strong implementation mechanisms at all levels of government, as well as the availability of technically qualified staff to collect and analyze early warning data. Moreover, to employ data collected through a bottom-up early warning process to inform early action, it is necessary to establish a clear connection between the collected data and drought-related need; this requires detailed data analysis, as well as a process to store and share data. Moreover, in employing a bottom-up early warning system within a drought response framework, it may be useful to combine and triangulate results obtained from different data sources, to enable the verification and comparison of results obtained through each source of early warning data individually.

# IV.5 Hotspots assessments

Additionally to the use of the LEAP and LIAS tools to detect droughts and their impacts, the NDRMC (former EWRD) conducts hotspots assessments on a quarterly basis. The objective of the assessments is to monitor the food security situation in Ethiopia, to be able to initiate early action where hotspots are identified. Hotspots assessments as well as other _ad hoc_ assessments can also be launched by the NDRMC to monitor
1,431|
|(17)|Number of times moved shelter|0.47|0.44|0.03|(0.08)|1,616|
|(18)|Permanent shelter (=1)<br>_Joint signifcance p-val:_ 0.102|0.91|0.86|0.05|(0.06)|1,616|

_Notes_ : This table reports treatment balance results for the 1,619 household interviewed at midline from the 2,017 households sampled for the study (see Table 3). We have admin data from the implementing partner’s integrated assessment but only for the 1,619 households that we were able to reach at midline. The first two columns report averages by group, while the third column reports results of estimating _yi_ = _β_ 0 + _β_ 1 _Ti_ + _εi_ . Robust standard errors in parentheses in the forth column and are clustered at the locality level. The last column reports the total sample size. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All outcomes are taken from the implementing organization’s data, except for gender and age, which are taken from the phone survey. “USD” denotes United States Dollars and “PPP” stands for Purchasing Power Parity, which is used to compare the absolute purchasing power of countries’ currencies. Rent is winsorized at the top 1% of values, in order to limit the influence of outliers. Disability is calculated using the Washington Group Short Set on Disability and uses the recommended cut-off (level 3) to classify respondents as being disabled or not based on six domains: seeing, hearing, walking, cognition, self-care, and communication. For household size, we drop one observation in which the number of individuals at baseline is reported to be 300. For times changed shelter, we drop one observation in which
# **Table 9. Well-Being**

|||**M**|**idline Surve**|**y**||**End**|**line Survey**||
|---|---|---|---|---|---|---|---|---|
|||Control<br>Group<br>|Treatment<br>|- Control<br>||Control<br>Group<br>|Treatment<br>|- Control<br>|
||Obs|Mean|Coeff.|_p-value_|Obs|Mean|Coeff.|_p-value_|
|**Beneficiary woman**|||||||||
|Self-esteem indicator (Rosenberg Scale)|n.a|n.a|n.a|n.a|812|21.29|0.01|_0.971_|
|Mental Health indicator|428|14.21|0.15|_0.644_|793|14.65|-0.26|_0.230_|
|**Husband**|||||||||
|Self-esteem indicator (Rosenberg Scale)|n.a|n.a|n.a|n.a|602|21.55|-0.03|_0.901_|
|Mental Health indicator|306|13.64|0.70|_0.137_|612|14.77|-0.18|_0.476_|

Notes _:_ Sample of mental health outcomes at midline: households surveyed in rounds 3 and 4. Coefficients from an OLS regression of the lefthand side variable on a treatment dummy, controlling for strata dummies, survey-time dummies, and baseline traits (see Table 3 note). ***, **, *
# _Panel Estimates_

As mentioned before, the data on remittances are available on a panel basis, but data on transaction costs and on the number of migrant workers are only available for a cross-section. Nevertheless, building on the results in Table 3 on the determinants of transaction costs, we now try to make more efficient use of the available information. As a first approach, we use the estimated coefficients from Table 3, combined with historical panel data on the independent variables, to “backcast” transaction costs. Let us call this backcast pseudo-variable “predicted costs.” We then run the panel regression _* (6) WRi,t_ = α _i_ + β 0 Y _i,t_ + β 1 _yi,t_ + β 2 _yi,t_ + β 3 _DualERi,t_ + β 4 _PredCosti,t_ + β 5 _NEOt_ + β 6 _t_ + ε _i,t_ , where the variables are defined above, and α _i_ denotes the country-specific fixed effects; _PredCost_ denotes the predicted costs; NEO is Net Errors and Omissions component of the Balance of Payments, which will control for changes in measurement of remittances (see below), and _t_ indexes the relevant year. The equation is estimated over the 1995– 2003 period. The specification implicitly assumes that the country-specific fixed effects will adequately capture the impact of the number of migrant workers. The results are presented in Table 4, columns 1 and 3. Predicted costs and the dual exchange rate have the expected sign and are statistically significant.

As an alternative approach, rather than “backcasting” transaction costs and then using the predicted transaction costs as a regressor for remittances, we allow all variables used to explain transaction costs in Table 3 to enter _directly_ as regressors for remittances. This approach might be more robust to errors in measuring transaction costs. Specifically, we run the panel regression 19
|**11.6**|**0.05**|

_Source:_ Teravaninthorn and Raballand 2009.

_Note:_<sup>*</sup> Implicit velocity is the total distance divided by the total time taken to make the trip, including time spent stationary at ports, border crossings, and other stops.

The volume of goods carried on southern African corridors per kilometer is significantly higher than all other parts of Africa—as much as three times higher than West Africa, the region with the secondlargest traffic volumes. This suggests that the Southern African Development Community (SADC) region benefits from scale economies in regional road freight transportation to a greater extent than is possible in other parts of Africa.

Transit corridors in southern Africa perform significantly better than in other parts of Africa. Road freight tariffs, at 5 cents per tonne-km in southern Africa, are the lowest in the continent; meanwhile, an effective velocity of 11.6 miles per hour allows goods to move faster than in other parts of the continent. On select corridors in the SADC, such as from Durban to Lusaka, price and performance levels approach those of regions outside Africa. Yet, as will be discussed below, transit times leave much to be desired.

The 2010 Logistics Performance Index (LPI) confirms that the costs associated with overall logistics in southern Africa are lower than those in East and West Africa but marginally higher than those of the average lower-middle-income country in the world.<sup>2</sup> Within southern Africa one finds variation at the country level. South Africa has logistics systems that are comparable with those of Europe, but other countries, by virtue of being island states or landlocked, have very low connectivity. Namibia and Angola are among the worst performers globally. Logistics in southern Africa also suffer from high import costs, the second highest in the world after West Africa (World Bank 2010a; 2010b).

> 2 LPI is an annual survey of international freight forwarders. The
important piece of information into account when formulating policies aimed at promoting FLFP.

# 3. Data and methods

Different data sources are used for this paper. The macro analysis (first part of Section 4) is conducted using the World Bank’s PovcalNet<sup>3</sup> database. The analysis was limited to observations after 2002, as well as to countries with data on GDP and other basic socio-economic variables. This reduces the sample to about 127 countries, encompassing a range of low-to-high income

> 3 The PovCal database covers the period since 1978 and includes low-income, lower middle-income, upper-middleincome countries and high-income countries. Most of the Gini observations are calculated from direct access to household surveys but clearly countries do not collected data in the same years and the time gap varies across countries. The panel is therefore an unbalanced one.

6
|(0.040)|(0.142)|(0.045)|(0.135)|
|WY _p_-value|0.020|0.080|0.000|0.460|
|Control mean|0.920|0.000|0.803|-0.000|
|Observations|244|244|732|732|
|_Information experim_|_ent_||||
|Treatment (1 year)|0.149***|0.309**|0.027|0.294*|
||(0.046)|(0.152)|(0.055)|(0.164)|
|WY _p_-value|0.000|0.040|0.500|0.120|
|Control mean|0.820|0.000|0.777|-0.000|
|Observations|174|174|522|522|
|_Single grant observa_|_tion_||||
|Treatment (1 year)|-0.007|-0.446***|0.027|-0.192|
||(0.034)|(0.170)|(0.070)|(0.193)|
|WY _p_-value|0.900|0.000|0.840|0.640|
|Control mean|0.962|0.337|0.778|0.209|
|Observations|151|151|453|453|

Notes: Robust standard errors clustered by school in parentheses. Westfall and Young (WY) p-values reported. Data source: parent association survey and teacher survey.* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01
Policy Research Working Paper 10572

# **Abstract**

This study investigates how the landscape of sex work in Dar es Salaam, Tanzania, evolved in the context of the COVID-19 epidemic. Using a mixed-methods approach, the analysis triangulates data from quantitative and qualitative sources to quantify shifts in income, demand, and client frequency and describe female sex workers’ perspectives on their work environment. The COVID-19 restrictions introduced in early 2020 resulted in dramatic decreases in sex work income, leading to extreme financial vulnerability, food insecurity, and challenges in meeting other basic needs such as paying rent. However, in a 2021 follow-up survey, sex workers reported the summer of 2021 as a key turning point, with the demand for sex work rebounding to closer to pre-pandemic levels. Notably, despite the average number of unique weekly clients not yet having fully rebounded, by 2021 the price per client and the total monthly sex work income had returned to pre-pandemic levels. This may potentially be explained by an increased number of repeat clients, which represented a larger proportion of all clients during the COVID-19 pandemic.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at at mbalampama@pactworld.org; ddewalque@worldbank.org; wdow@berkeley.edu; and rebeccahemono@berkeley.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do
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example, a one-standard deviation higher level of private sector credit is associated with about 70% higher buyout investment and a major investor protection reform results in about a doubling of subsequent buyout investment.

Lastly, having documented the positive impact of regulatory reforms on the amount of buyout investments, we also explore if the impact of reforms varies among countries with different legal conditions and human capital levels. On the one hand, a country with weaker existing governance may benefit more from the implementation of reforms; on the other hand, for these regulatory reforms to be effective in attracting more buyout capital, a country may need to have a strong country governance structure in place. Suggesting that reforms indeed need to be supported by strong country governance, we find reforms to be more effective in countries with better regulatory quality, rule of law, and lower corruption. Additionally, we also explore if the level of education and human capital plays a role in how effective the reforms are in attracting buyout activity. We find the positive association between reforms and buyout investments to be more pronounced in countries with higher levels of education, suggesting that reforms need to be backed not only by a strong regulatory environment but also availability of high-quality human capital.

Despite the fact that private equity has increasingly become a global asset class playing an important role in capital formation, there is yet very little evidence on the determinants of global private equity capital flows across countries and industries. The limited existing literature mainly focuses on venture capital (VC) investments and finds country-level factors such as the legal environment (Wright et al, 2004; Guler and Guillen, 2005) and stock market liquidity (Black and Gilson, 1998; Jeng and Wells, 2000; Cumming et al, 2009) to be important drivers of venture capital activity. This paper contributes to the limited existing literature on the determinants of private equity activity across countries, which mainly consists of studies that use surveys of private 5
. Second, even when measured, these readings may subsequently be doctored due to political motives, such as meeting mandatory targets of energy intensity (Ghanem and Zhang, 2014).

These features are some of the primary reasons why “top-down” measurement of air pollution from satellites has become prevalent in scientific studies (Lin and McElroy, 2011; Wang et al., 2012). NO2 specifically has been of keen interest to scientists, and top-down measurements have proven fruitful in several contexts. For example, to infer atmospheric densities over China, where monitoring station data is suspect (Richter et al., 2005). An air pollutant with serious implications for the environment and health, near-Earth NO2 is primarily due to human economic activities involving combustion (e.g., vehicles, power plants, and factories).

Still, the availability of satellite readings of other air pollutants more familiar to some people, including carbon dioxide (CO2), begs the question of why satellite readings of air pollution have

> (2020) note a correlation between NO2 and economic activities in select U.S. cities during the COVID-19 crisis. No other work generalizes these results globally, develops an NO2-based measure of economic activity, or considers our applications.

4
Figure 2: All-India Total Factor Productivity <!-- Start of picture text --> All−India Total Factor Productivity<br>Year<br>Aggregate Average<br>Reallocation<br>19851986198719881989199019911992199319941995199619971998199920002001200220032004<br>.2<br>.15<br>.1<br>TFP<br>.05<br>0<br>−.05<br><!-- End of picture text --> Total factor productivity (TFP) decompositions for the population of firms, conducted at the allIndia level, using the OP method. “Aggregate” indicates market-share weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. Source: Authors’ calculations based on Annual Survey of Industries data.

36
/sup><br>(0.047)|
|Observations|16532|16532|16532|18488|18488|18488|15168|15168|
|Year FE|||✓|✓||✓||✓|
|Num exiters|52|52|52|63|63|63|50|50|

**Note:** This table illustrates the relationship between exit probability and firm linkages. Exiting firms are entities which are not observed in any subsequent year during our sample period. All columns include a control for log pre-period annual revenue, measured in constant 2020 U.S. dollars. Columns (7) to (8) use the two-period lag. Columns (3), (4), (6), and (8) include year fixed effects. Column (1)-(3) test for a relation between the number of ownership-connected firms and firm exit, including the variable for supply chain linkages from column (2). Column (4) replaces the log owner ties variable with a dummy equal to one if a firm has ownership linkages to an agribusiness. Columns (5) and (6) replace the weighted degree measure with the unweighted degree count, i.e. a simple dummy for two firms being connected in a given period. Columns (7) and (8) show the specification with two rather than one lags. Robust standard errors are applied.

Overall, our findings highlight the importance of a well-connected network in mitigating the risks associated with potential industry exit, providing essential insights for firms

> 24We observe a total of 28 extinct exiters without surviving ultimate parent. This number reduces to 26 when considering lagged links. Only three of these firms have available sales data, limiting the feasibility of statistical inference.

27
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