rafmacalaba/data-use-mentions-extended huggingface dataset:
sup>|10.3%|19.2%|7.0%|13.6%|515|

(d) Rate of harassment within 500m but excluding nearest 200m.

(u) Observation counts are un-weighted.

The specific geo-location data available from the survey also allow for additional analysis using GIS<sup>33</sup> software. Appendix A.IV shows heat maps that help visualize the spatial occurrence of key variables. The first panel shows the occurrence of all enumerated informal businesses (whether given the long-form survey or not): the map shows clear clustering, a construction of the data collection process. The remaining three panels in red, purple, and orange show heat maps of the occurrence of BRIBE and HARASS, respectively. One notes immediately that these are not an exact

> 31 ZimStat’s 2014 Labour Force Survey (LFCLS) reports that 68% of workers in the informal sector had their highest level of education in as some form of secondary school, completed or not (Form 1-6, with 46% terminal at Form 4) (ZimStat, p. 92)

> 32 This compares to a figure of 52% from the ZimStat LFCLS (p. 87). Discrepancies can likely be explained by composition effects (i.e. the LFCLS covers areas outside of Harare), but these values are within confidence ranges. 33 That is, Geographical Information Systems.

(20)
the city the 7th most congested in the world in 2019. Similarly, New Delhi was ranked the 8th most congested city of the world, according to the 2019 TomTom traffic congestion index. Together, while congestion remains a pressing policy challenge in these cities, existing policies are either politically difficult to adopt (e.g., congestion pricing) or have yet to deliver on their promises.

> 8 Inrix ranks cities according to the number of hours spent in congestion (see www.inrix.com).

20
Coconut” category, restrict the “Dried fish” group to only “dried sardines,” excluding larger fish, and so on. This removes much of the bias from grouping goods of different variety under a single heading. The end result is a data set in which items are far more uniform than those in a typical consumption survey.

We also drop items with too few observations, and drop perishable items that cannot be stored for two weeks by most households (such as beef, milk, and fresh fish). We do not drop or retain items based on whether the price schedule exhibits bulk discounts. After selecting the items, we remove outliers by dropping any observations for which the quantity or unit price is more than 4 standard deviations from the item-level mean, and dropping

> 6More details are available at the project page, accessible here: http://edi-global.com/publications/. Data are available by contacting the lead SHWALITA researchers listed on the project page.

11
<!-- Start of picture text --> Thousands of cases<br>1400 c ot<br>1200 Tmnergy exporters<br>—Other EMDEs<br>1000<br>800<br>600<br>400<br>200<br>0<br>1-Mar 16-Mar 31-Mar 15-Apr 30-Apr 15-May<br><!-- End of picture text --> <!-- Start of picture text --> Index<br>100<br>90<br>80<br>70<br>60<br>50<br>40 —Energy exporters<br>0 —Other EMDEs<br>1<br>)<br>1-Mar 16-Mar 31-Mar 15-Apr 30-Apr 15-May<br><!-- End of picture text --> <!-- Start of picture text --> Thousands of fatalities<br>60<br>—Energy exporters<br>50<br>oth EMDE<br>—Other Ss<br>40<br>30<br>20<br>10<br>0<br>1-Mar 16-Mar 31-Mar 15-Apr 30-Apr 15-May<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP Percent of employment<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Energy Other | Energy Other<br>exporters exporters<br>GDP Employment (RHS)<br><!-- End of picture text -->
enjoy better access to infrastructure facilities and sanitation systems such as flush toilets and piped water. Employment rates are not substantially different across income groups, but the composition of employment does vary with a higher incidence of public sector employment among the middle and upper income groups.

Since households in the experimental sub-sample are those who could still be located eight years later, Appendix Table 1 compares the main outcome variables for the original sample of 300 households to the experimental subsample of 205 households. The means are computed for the first wave of weekly surveys completed in 2000-01. The number of ―acute‖ illnesses refers to the total number of acute illnesses reported in the preceding month, and weekly reports are aggregated up to obtain monthly totals. Since aggregated measures could overstate the recall effect if episodes last longer than a week, we will also examine two other outcome measures—whether or not at least one acute illness was reported and the duration of acute illness— that should not be affected by this aggregation bias. Our measure of the labor and schooling impacts of sickness are based on reports indicating that at least one day of work (or school) was lost due to an acute illness during the relevant recall period; for brevity, we refer to this variable as the ―labor impact of acute illness‖.

For our full sample, based on the weekly data, individuals report just less than 1 acute episode a month, and about 47 percent report at least one episode. The average time spent sick every month is 4 days and individuals report losing at least one day of work/school in 7.8 percent of weeks due to an acute illness. Das and Sánchez (2004) report the types of acute illnesses that were reported during the weekly surveys; the large majority is respiratory illnesses, non-specific myalgias, dermatological disorders and ENT infections. Given that (a) doctors very seldom tell their patients what they are suffering from and (b) even when they do, they is a high probability that they are incorrect,
winner of the global flirting with PPI during the 1990s. It leads in all categories. Particularly impressive is the per capita figure. East Asia has done quite well in terms of total investment levels and number of projects but per capita, the numbers are clearly not as impressive. As expected, South Asia and Sub-Saharan Africa, the two globally poorest regions have been the main losers. Although the Middle East appears to be at the bottom of the distribution of the investment payoffs of reform, its per capita payoff is twice as large as for Africa and two an a half times larger than for South Asia. Eastern Europe has done somewhat surprisingly well per capita.

Table 3: Selected Indicators of Regional distribution of PPPI

||Total investment<br>commitment (US$ billion)|Number of<br>Projects|Average Project Size<br>(in US$ million)|Accumulated Investment<br>commitment per capita as of<br>2002 (in US$)|
|---|---|---|---|---|
|East Asia|182|687|265|99|
|Eastern Europe|100|589|169|211|
|Latin & Central<br>America|368|978|376|694|
|Middle East|24|64|312|79|
|South Asia|42|195|213|29|
|Sub-Saharan Africa|26|213|122|38|
|Total|741|2726|272|141|

Source: Author Calculations from World Bank PPI database and World Development Indicators Looking at sectors the story of the winners and losers is also quite clear as can be seen from Table 4. In terms of investment commitments and average project size, the telecoms sector is the clear winner of the PPPI experience so far. In terms of the number of projects, however, telecoms lag both the energy and the transport sector. The clear loser of the experience
years:<br>Mean: 88.47<br>Median: 95.00<br>Skewness: -1.14<br>15 Default years:<br>Mean: 35.09<br>Median: 25.00<br>Skewness: 0.97<br>10<br>5<br>0<br>0 50 100 150<br>Bond prices (end of year)<br>Non-default years Default years<br>Percent Percent<br>Percent<br><!-- End of picture text --> _Notes:_ This upper part of this figure shows the distribution of yearly real returns. Panel A uses aggregate yearly returns based on the global sovereign bond portfolio. Panel B uses bond-level data and distinguishes between years in default and non-default years. Panel C shows the distribution of bond prices as reported in the primary sources (mixing USD and British Pound prices) for default and non-default years and also using bond-level data.

In default years (Panel B), the distribution differs markedly, with a lower average real return and much fatter tails. However, the coefficient for skewness remains close to zero, suggesting that years of investor losses and years of recoveries balance each other out in normal times and in crisis times (see also Section 4 on bond performance following a default). Furthermore, Panel C shows that there is much more skew 25
suggestive evidence that our core results are not driven by reverse causality.

To understand the mechanisms driving this negative e↵ect, we then investigate four channels suggested by incentive theory through which the detrimental impacts of management practices related to incentives/monitoring for bureaucrats might occur. First, bureaucrats might need to multi-task in their work environment, needing to exert some types of e↵ort that can be labelled as ‘processing’ and does not directly increase project completion rates, while also needing to exert more productive types of e↵ort that do raise project completion rates. Our management practice measure might then capture an incentive system that places excessive regulatory burden or ‘red tape’ on bureaucrats that has been argued to cause bureaucrats to mis-allocate e↵ort towards processing activities [Kelman 1990, Baker 2002]. Consistent with this, we find the negative partial correlation between incentives/monitoring and project completion rates is even _more negative_ when bureaucrats need to coordinate/negotiate internally with other bureaucrats in their organization, or need to coordinate with external stakeholders.

Second, incentives/monitoring might crowd out the intrinsic motivation of bureaucrats. To investigate this, as part of our civil servants survey we measured the public service motivation (PSM) of bureaucrats, using an abbreviated version of the standard Perry scale [Perry 1996]. We find the negative impact of management practices related to incentives/monitoring are partly ameliorated when bureaucrats in the organization score higher on the PSM dimensions of ‘compassion’ and ‘public interest’. As in Ashraf _et al._ [2014], our evidence thus suggests incentives/monitoring _crowd-in_ the e↵ort of intrinsically motivated bureaucrats, partly (but not entirely) ameliorating the negative e↵ects of practices related to incentives/monitoring.

Third, management practices related to incentives/monitoring might be subject to _subjective_ performance evaluation (SPE). While SPE has the benefit of being based on a more holistic set of assessments, such subjective assessments also give rise to other biases and dysfunctional responses, especially
2000). They used mass media, lobbying efforts, and social mobilization to pressure Ministers of Justice and Safety, members of those departments, the Task Team in charge of implementing the legislation, the police, and the judiciary. Based largely on key informant interviews, Usdin et al. (2000) concluded that the campaign contributed to more timely and effective implementation of the act.

## **Individual behavior change strategies**

## **_Campaigns to increase public awareness of and support for women’s legal rights_**

An essential component of effective legislative reform is to raise awareness of and build support for new laws among the general population. Increasing access to justice requires that women know their rights and feel that they can exercise them. Thus, many NGOs have launched media and legal literacy campaigns to raise awareness of new legislation and encourage women to exercise their rights. Wellevaluated initiatives include Soul City (South Africa) and the Nicaraguan Network of Women against Violence. Both have demonstrated an impact on knowledge of and attitudes towards women’s rights through large population-based surveys, including raising awareness that violence against women is a crime, and knowledge of specific legislation (Scheepers, 2001; Scheepers and Christophides, 2001; Muirhead, Kumaranayake, and Watts, 2001; Ellsberg, Liljestrand and Winkvist, 1997; Velzeboer et al., 2003).

# **HEALTH**

## **Background**

Historically, the health sector was slow to recognize the public health implications of violence against women. Even today, many medical and nursing schools do not adequately prepare professionals to recognize or respond to the health consequences of domestic violence, rape or sexual abuse; providers often view violence against women as a social problem and hesitate to discuss physical or sexual violence with patients; and many organizations do not equip personnel to respond appropriately to girls and women who disclose violence (Heise, Ellsberg and Gottemoeller, 1999). Mensch, Bruce and Greene (1998) argue that most reproductive health initiatives for young
# Figure 1

<!-- Start of picture text --> GDP per capita of Sub-Saharan Africa and NIEA(8), 1960-99<br>Dollars ( Measured at 1987 prices and exchange rates)<br>2500<br>Average annual growth 1960-99<br>Sub-Saharan Africa<br>NIEA(8) 0.5<br>4.7 NIEA(8)<br>2000 Chinese Taipei 6.6<br> Hong Kong<br> Indonesia 5.3<br> Korea, Rep. of 3.6<br> Malaysia 5.9<br> Philippines 3.9<br>1500 Singapore 1.1<br> Thailand 6.4<br>4.6<br>1000<br>Sub-Saharan Africa<br>500<br>0<br>Note: NIEA(8) (Newly Industrialized Economies in Asia) consist of Chinese Taipei, Hong Kong China, Indonesia,<br>Rep. of Korea, Malaysia, Philippines, Singapore and Thailand.<br>Source: WTO and the World Bank, World Development Indicators 2000.<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998<br><!-- End of picture text --> 35
buildings (including energy efficiency measures, heat pumps, solar water heaters etc.). In addition, the projected annual energy consumption expenditure of households obtained from the energy forecast model mentioned in Section 3.1 is introduced to the IO model to estimate the multiplier effect of changes in private consumption. Tables in Appendix B present the distribution of annual spending associated with investments and private consumption by sector of economic activity under the alternative energyrelated recovery measures.

The initial static equilibrium conditions of the IO model, which serve as the reference case, are based on the latest available national symmetric IO table of Cyprus for the year 2016. The national table, which includes 65 sectors of economic activity, was aggregated into 20 economic sectors, which is presented in Appendix B (Table B. 11). The demand growth rates for the economic sectors are defined based on the GDP projections for the period up to 2030, including the impact of the COVID-19 pandemic, and were obtained from the Ministry of Finance. Specifically, for 2020 we assumed a decline in the growth of the Cypriot economy (-7.4%) across all economic sectors, and for 2021 a strong economy-wide recovery (+6.1%). From 2023 onwards, growth rates return to usual levels following the official national macroeconomic outlook.

Some of the recovery measures listed in Table 1 involve energy savings and hence induce a decrease in private consumption for energy, traded products, and services. We assumed that this reduction of spending, after accounting for household savings,<sup>4</sup> will return to the economy and induce a rise in

> 4 We assumed that the household saving rate is 2.4% of disposable income in Cyprus, in line with Eurostat (2020).

13
A., M. Tariverdi, M. Obolensky, X. Chen, R. Vincent, S. Malgioglio, J. Rentschler, S. Hallegatte, N. Yoshida (2019), Wading Out the Storm: The Role of Poverty in Exposure, Vulnerability and Resilience to Floods in Dar Es Salaam. World Bank Policy Research Working Paper No. 8976,

- Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN. (2018). Available at: http://ciesin.columbia.edu/data/hrsl/#acknowledgements. (Accessed: 15<sup>th</sup> September 2020).

- Fielding, J. (2012). Inequalities in exposure and awareness of flood risk in England and Wales. Disasters. Vol. 36(3), pp.477-494

- Freire, Sergio; MacManus, Kytt; Pesaresi, Martino; Doxsey-Whitfield, Erin; Mills, Jane (2016): Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; Association of Geographic Information Laboratories in Europe (AGILE). AGILE 2016.

- Daniel, V. E., R. J. G. M. Florax, and P. Rietveld. 2009. “Flooding Risk and Housing Values: An Economic Assessment of Environmental Hazard.” Ecological Economics 69 (2): 355–65.

- Durand-Lasserve, A., H. Selod, and M. Durand-Lasserve. 2013. “A Systemic Analysis of Land Markets and Land Institutions in West African Cities: Rules and Practices—The Case of Bamako, Mali.” Policy Research Working Paper 6687, World Bank, Washington, DC.

- Hallegatte, S., A. Vogt-Schilb, J. Rozenberg, M. Bangalore, C. Beaudet (2020). "From Poverty to Disaster and Back: a Review of the Literature," Economics of Disasters and Climate Change, Springer, vol. 4(1), pages
|90.1|32.0|
|Central|4,220|75.2|16.9|98.2|39.5|
|South|5,736|70.1|4.7|96.3|37.8|
|Malawi|12,447|71.7|10.8|96.6|38.0|
|**2010/11**||||||
|_Urban-Rural_||||||
|Urban|2,233|33.0|3.4|97.6|16.5|
|Rural|10,038|87.2|14.2|97.0|31.1|
|_Region:_||||||
|North|2,302|79.2|14.8|94.0|27.5|
|Central|4,217|83.9|17.3|97.6|27.7|
|South|5,752|74.1|7.6|97.4|30.1|
|Malawi|12,271|78.8|12.5|97.1|28.8|

_Source_ : Authors’ calculations based on IHS3 and IHS4 data. _Note:_ HH = households.

While most farm households produce maize, the share of households engaged in maize sales is minimal, indicating that many of the maize producers are subsistence farmers, producing mainly for their consumption. Even if 72 percent of households grew maize in 2016/17, only 11 percent of households sold maize. Across regions, a larger share of households in the rural Central region (19 percent) engages in maize sales, compared to the South region (5 percent). To make matters worse, farm households typically sell their maize immediately after harvest, when prices are generally low. Maize harvest usually occurs during March–June
Policy Research Working Paper 8811

# **Abstract**

E-commerce has developed rapidly in China, and Taobao Villages, which are villages significantly engaged in e-commerce, are prospering in rural areas. E-commerce is fostering entrepreneurship and creating flexible and inclusive employment opportunities, including for women and youth. This paper examines the role of e-commerce participation in household income growth, drawing from a survey of representative Taobao Villages in 2017. The paper presents three main findings. First, e-commerce participation is not random: participation is higher among the households with younger household heads, with secondary education, particularly those with technical and vocational education, urban work experience, and knowledge of e-commerce. Second, e-commerce participation is associated with higher household income, with some indications that participation has a strong positive effect on household incomes. Third, e-commerce appears to yield benefits that are broadly shared among participants in an equitable way in Taobao Villages.

This paper is a product of the Poverty and Equity Global Practice. 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/research. The authors may be contacted at xluo@worldbank.org and cniu@worldbank.org.

_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 not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
8 Data analysis was conducted in STATA version 15.1.<sup>22</sup> We first descriptively explored demographic characteristics of participants and self-reported sexual behavior and sexual health history, including number and type of clients, income, condom use, prevalence of STIs, and previous STI and HIV testing. Characteristics between treatment groups and baseline and endline participants were compared.

The primary intent-to-treat analysis included only participants who completed the study, as the primary outcome of combined HIV/HSV2 incidence was measured at endline. Linear probability models were used to estimate unadjusted and regression adjusted risk differences (RD) with 95% confidence intervals (CI). Control variables in the adjusted models were baseline: age, education, amount of time living in Dar es Salaam, marital status, number of children, total monthly income, monthly income from sex work, social ladder ranking, location of sex work, number of clients, price received for sex with a condom, previous STI diagnosis, timing of last STI testing, preferred frequency of STI testing, previous HIV testing, perceived HIV risk, and baseline value for the dependent variable being analyzed.

In sensitivity analyses, we also report results of the adjusted models for the primary outcome after applying inverse probability of censoring weighting (IPCW) to account for systematic observed differences in participants who were lost to follow-up. The probability of participating in endline (not attriting) was generated using logistic regression as a function of the same baseline control variables listed above. Weights were calculated using the predicted probability of not attriting and were included in adjusted analyses.

Finally, for our main endpoint (combined HIV/HSV2 incidence), we also estimate unadjusted treatment effect bounds following Lee (2009),<sup>23</sup> using the _leebounds_ command in Stata. We report these upper and lower bound treatment effect estimates which vary the assumptions regarding nonrandom selection of the FSW who attrited from the endline survey. This is a nonparametric bounding that drops a set of the Treatment arm
|(0.067)|(0.033)|(0.056)|(0.081)|(0.088)|(0.069)|
|Interest rate diff.|0.053|0.111**|0.037|-0.005|0.017|0.238***|
||(0.079)|(0.045)|(0.066)|(0.107)|(0.104)|(0.092)|
|VIX|-0.033|0.031**|-0.060***|-0.046|0.061*|-0.056*|
||(0.027)|(0.015)|(0.023)|(0.036)|(0.035)|(0.031)|
|Country fixed effects|Y|Y|Y|Y|Y|Y|
|No. of obs.|781|859|781|859|781|859|
|No. of countries|26|26|26|26|26|26|
|Adj. R-squared|0.347|0.333|0.186|0.051|0.158|0.115|

Notes: All regressions are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticityconsistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.<sup>†</sup> Lagged value corresponds to the inflow category in the row header.<sup>††</sup> _Net other investment inflows_ (Balance