rafmacalaba/data-use-mentions-extended huggingface dataset:
elections or other transitions of power. Careful monitoring of perceptions may allow for proactive responses as opposed to waiting for losses to accumulate. The importance of risk and the limited ability of households and communities to address this risk suggest an important role for government policy. Moreover, since perceptions of the risk of violence are more important than the actual risk of violence, this suggests that conflict-related losses may persist even after the end of violence. These perceptions may also impact responses to post-conflict program which seek to introduce new livelihoods and assets.

While these results underline the importance of perceptions of risk, these data are rarely available even in nonconflict contexts. In contrast, there has been a strong increase in the availability of spatially and temporally disaggregated data on acts of violence. This article has introduced a simple methodology to obtain disaggregated measures of statistical risk using this newly available data. While the measures of statistical risk are not as strong as those of perceived risk, their results are qualitatively similar suggesting that this methodology can be used to examine responses to conflict risk in other settings.

Although this research causally links conflict-risk with lower economic outcomes, the precise pathways are unclear. The existing literature on livelihoods during conflicts suggests important endogenous responses to conflict risk, but these are obscured by the lack of measures of risk. Consequently, an important avenue for future research will be to precisely measure these responses and to understand whether these occur at the intensive and/or extensive margins. Similarly, this raises questions about the origins of the large and lasting micro-economic postconflict costs found in the literature. Are these the result of exposure to violence, as suggested by many articles, or the responses of households to the risk of violence? While the answers are beyond 30
Policy Research Working Paper 9886

# **Abstract**

The traditional consumer price index is often produced at an aggregate level, using data from few, highly urbanized, areas. As such, it poorly describes price trends in rural or poverty-stricken areas, where large populations may reside in fragile situations. Traditional price data collection also follows a deliberate sampling and measurement process that is not well suited for monitoring during crisis situations, when price stability may deteriorate rapidly. To gain realtime insights beyond what can be formally measured by traditional methods, this paper develops a machine-learning approach for imputation of ongoing subnational price surveys. The aim is to monitor inflation at the market level, relying only on incomplete and intermittent survey data. The capabilities are highlighted using World Food Programme surveys in 25 fragile and conflict-affected countries where real-time monthly food price data are not publicly available from official sources. The results are made available as a data set that covers more than 1200 markets and 43 food types. The local statistics provide a new granular view on important inflation events, including the World Food Price Crisis of 2007–08 and the surge in global inflation following the 2020 pandemic. The paper finds that imputations often achieve accuracy similar to direct measurement of prices. The estimates may provide new opportunities to investigate local price dynamics in markets where prices are sensitive to localized shocks and traditional data are not available.

This paper is a product of the Development Data Group, Development Economics and the Fragility, Conflict and Violence Global Theme. 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 author may be contacted at bandree@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,
# **_iv. Industry characteristics correlated with responsiveness to GQ_**

The ASI data permits studying the industry related characteristics of the plants most responsive to connectivity. To this end, we replicate equation 1 splitting the regression with sub-samples partitioned by median industry characteristics. Critically, each age group is well-represented across industry characteristics (Annex Table 5) so the previous results for young plants are not simply capturing newly arrived industries – the relevant characteristic is, in fact, youth.

This said, there are significant differences in the stimulative effect of connectivity across industry characteristics within age category. Table 3 shows that responsiveness is almost entirely concentrated in capital intensive industries for all measures of activity in young plants and for output in mature plants (Panel B), and to a lesser extent in land intensive industries (Panel C). There is less clear differentiation for human capital-intensive industries where responsiveness is higher only for output and plant size measured by employee count (panel D).

Consistent with the strong results for capital intensity, the table suggests that plants most responsive to connectivity are those producing durable goods (category including wood products; paper products; leather; textiles; rubber plastic products; motor vehicles, panel E). In particular, output is significantly more responsive for all age categories, while plant size is also significant for the young. Similar results are observed for sectors with strong backward and forward value chain linkages (panel F; e.g., textiles; wearing apparel; office computing; radio, television communication; motor vehicles); and for those that are the least amenable to remote work (panel G).

This said, responsive sectors are also low in contractual intensity (panel H), that is, the proportion of a sector’s intermediate inputs that require relationship-specific investments and differentiated products; and where relationship stickiness, as measured by firm to firm relationship durations, is low (panel I) particularly for output and firm size in the young plants.<sup>13</sup
|671,509|6.592|6.602|
|2007|51,708|661,500|754,661|6.620|6.618|
|2008|55,841|722,110|748,548|6.641|6.637|
|2009|58,870|724,383|785,676|6.674|6.696|
|2010|59,444|714,004|851,565|6.705|6.664|
|2011|58,577|678,884|859,973|6.765|6.697|
|_c. Ec_|_uador_|||||
|Year|Number of frms|Number of f|ormal workers|Average|log real wage (ppp)|
||IESS|IESS|ENEMDU|IESS|ENEMDU|
|2006|62,604|812,659|776,552|6.242|6.436|
|2007|70,755|894,719|786,785|6.278|6.461|
|2008|78,204|978,916|907,028|6.31|6.435|
|2009|87,858|1,094,927|987,285|6.372|6.436|
|2010|102,339|1,172,687|1,195,190|6.409|6.45|
|2011|126,161|1,314,478|1,416,680|6.425|6.488|
|2012|152,355|1,484,112|1,476,213|6.442|6.518|

_Note_ : The table reports the average log real wage and total number of workers, working in the private sector, in Brazil, Costa Rica, and Ecuador, using household surveys and
on the relationship between digital-tech-oriented firms and ethical practices across cultural dimensions. Therefore, we also add to the strand of literature examining the link between cultural dimensions and sustainability (Sedita et al., 2022; Kucharska and Kowalczyk, 2019; Lahuerta-Otero and González-Bravo, 2018; Parboteeah et al., 2012; Onel and Mukherjee, 2014; Husted, 2005; Vachon, 2010; Gallego-Álvarez and Ortas, 2017), as well as to studies focusing on the issue of regulation in sustainability (Behera and Sethi, 2022; Li et al., 2021), since we examine how regulatory burdens and perceptions of courts as an obstacle to business activity are related to firms’ ethical behavior.

The remainder of the paper is organized as follows. In section 2, we describe the data and variables; in section 3 we present our empirical approach; in section 4 we discuss our results. Finally, section 5 concludes the paper.

# **2 Data and Variables**

We collected data from various sources to analyze the correlation between firms’ technology and ethical practices. First, we gathered firm-level data from the World Bank Enterprise Surveys (ES), covering 158 countries from 2006 to 2023, for a total of 192,132 observations.<sup>1</sup> We consider firms in the manufacturing and nonmanufacturing industries as per the ISIC Code Revision 4 classification.<sup>2</sup> In addition,

> 1 The Enterprise Surveys are firm-level surveys of representative samples of the private sector around the world. Data is publicly available and can be retrieved at the following web-link: <u>https://www.enterprisesurveys.org</u> (last accessed: November 2024). 2 The United Nations Statistical Commission produces a standard classification of economic activities: the International Standard Industrial Classification of all economic activities (ISIC).

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SHS dissemination. Lessons from the Bangladesh case study can possibly be transferred to other countries while taking account of local conditions.

# 4. **The 2005 Bangladeshi national survey**

A national household survey was conducted in 2005 for monitoring and impact evaluation of the RERED project. Although this survey was conducted to study grid electrification impacts, it also collected information on SHS households, which allows us to undertake this study. In fact, this survey is nationally representative of the rural population and it is the first available large-scale national household survey that collects information on SHS purchases in developing countries where various donor-financed SHS programs have been implemented.

This survey covers 20,900 households in rural areas of all six administrative regions in Bangladesh, including 1,000 households who had purchased SHS under various subsidy programs implemented in rural Bangladesh since 2001. Table 1 summarizes the sample size and distribution of electricity access across the six administrative regions. Overall, about 62% of households have no electricity access, 6% with SHS and 31% with grid-electricity. Electricity access varies across regions, with the Barisal region having the highest concentration of nonelectrified household at about 73%, while the Chittagong region has the lowest at 53%.

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the share of food products for which aid was received, and indicator variables for bartering food. Fourth, since the Rohingya are completely dependent on humanitarian aid, the effects of camps are assessed on aid and assistance, including the following variables: receiving vouchers, food in-kind, assistance accessing services, and receiving household items. The descriptive statistics for aid-related outcomes (Panel C), underscore the vulnerability of the population near the camps, with a vast majority (at

> 8Details on the index construction can be found in the following link : https://dhsprogram.com/ topics/wealth-index/Wealth-Index-Construction.cfm.

16
said it was _Somewhat_ , _Fairly_ , or _Very Reliable_ , as compared with 58% in Venezuela, and only 50% in Mexico. The survey data clearly showed that across the board information services, both in terms of access to information and its quality, were deficient, in spite of the reforms.<sup>57</sup>

# <u>Conclusion: Evidence of an Association between Autonomy and Performance</u>

The cases demonstrate that the ARA model permits managers to take greater control over their organizations and manage them more effectively, though they also demonstrate that some ARAs, notably Peru, Kenya, and South Africa (and perhaps Venezuela before SENIAT’s autonomy was reduced), were more successful than others in improving their performance (Table 4 presents a summary of performance by indicator and ARA).<sup>58</sup>

# **TABLE 4. ARA PERFORMANCE: A SUMMARY COMPARISON**

||**Input Ind**<br>|**icators**<br>|**O**<br>|**utput and Ou**<br>|**tcome Indicat**<br>|**ors**<br>|
|---|---|---|---|---|---|---|
||Personnel<br>Management|Collection<br>Costs|Collections|Registration|Compliance|Taxpayer<br>Services|
|**KRA**|•Quasi-<br>voluntary<br>retrenchment<br>•Overall staff<br>reduction<br>•Selective salary<br>increase<br>•Financial<br>support for staff<br>training|•Very low<br>collection<br>costs|•Declining<br>tax/GDP<br>ratio with<br>falling tax<br>rates (
assumed to receive transfers. Per student expenditure data are taken from Annual Education Statistics 2018 (for boarding schools and day schools at the primary and secondary level) and State of Tertiary Education in Bhutan 2017 (for tertiary level).

### **_Health_**

Since Bhutan’s health sector is managed and financed by the government, all households are considered as beneficiaries. We allocate health transfers using an insurance-value approach in which the same per capita spending is assigned to those sharing the same characteristic. Since per capita health care expenditure was only available by age group, we apply the transfer amount to all individuals in the survey based on their age. Variation in the quality of services or different valuations of health services across the welfare distribution are not considered.<sup>24</sup>

# 4. The Distributional Impact of Taxes and Social Spending

This section starts by describing the overall distributive impacts of taxes and social spending on poverty and inequality; these are then benchmarked against other countries. The second half of the section focuses on the disaggregated impact of individual policies.

## 4.1. Impact on Poverty and Inequality

Taxes and social spending combined contribute to a decrease in inequality in Bhutan. Figure 2 presents the Gini index of inequality for each income concept described in Figure 1. The Gini index based on market income (or gross market income plus pension) is 38.7 prior to any fiscal intervention; this is assuming that contributory pensions, mainly an entitlement for civil servants, are treated as deferred income. The Gini index slightly declines to 37.8 once direct transfers, personal income taxes and social security contributions are taken into account, and further to 36.1 when indirect taxes and subsidies are considered. The largest equalizing effect is achieved through in-kind transfers, which in Bhutan are mainly the benefits stemming from education and health services—the Gini index based on final income is notably lower at 31.3. The overall reduction in inequality is equivalent to around 7.4
Despite these issues, failing to line up poverty and population estimates is also problematic. In particular, pooling estimates from different years would bias the global profile towards characteristics of the poor in older surveys, many of whom might have since escaped poverty. For example, the most recent survey from India dates from 2011/12. Survey-based estimates show a fall in rural poverty from 36.3% to 24.8% between 2009/10 and 2011/12, and the “lined up” estimate for 2013 is 19.9%. Even though this latter estimate may overestimate poverty reduction, failing to adjust for this dramatic decline would over-represent poor rural Indians in the global poverty profile. At the same time, failing to account for population growth between 2010/11 and 2013 would underrepresent India relative to other countries.

Although we know of no systematic evidence that using this line-up method improves the accuracy of the poverty profile, our main specification is based on the “lined up” figures available on the PovcalNet website. In other words, we take the 2013 poverty rates published by the World Bank and use the corresponding percentile value to set poverty lines in the GMD sample. This ensures consistency with the published estimates and eases interpretation of the results. It also maintains continuity with the approach adopted in Olinto et al. (2013), except that we implement an additional population adjustment. In particular, the sample weight is adjusted to match population projections for each gender and age group in each country as of 2013, taken from the United Nations Department of Economic and Social Affairs (UNDESA).<sup>19</sup> In cases where population estimates by age group and gender are not available from UNDESA, we simply re-scale the sample weight to match the total population reported in the World Development Indicators. Section 3 below reports estimates based on this method.

In section 4, we test the sensitivity of key results to the choice of line-up method. In particular, we compare key results to an
Inequality, Economic Growth and Nonrenewable Resources magnitude of the coefficient generally increases after the feedback effect has been taken into account.

Table 7: Estimated Results from the Non-Recursive Model Specification (baseline model)

||**IV Fixe**<br>|**d Effect**<br>|**3S**<br>|**LS**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDPc**|
|Non Renewable resource dependence|-0.149***|-0.443***|-0.151***|-0.467***|
|High-low education ratio|-0.082|-|-1.110***|-|
|High-low education ratio square|0.008|-|0.025***|-|
|Consumer price index (2005)|0.005|-|0.027***|-|
|Services value added share|0.072|-|0.035|-|
|Manufacturing value added share|0.055|-|0.145**|-|
|Agriculture value added share|-0.124**|-|-0.105**|-|
|GDP|-0.040|-|-0.005|-|
|Log labor force|-|-11.564**|-|-9.069**|
|Log gross fixed capital formation|-|9.842***|-|7.658***|
|Log exports of goods and services|-|1.344|-|3.123**|
|Log REER|-
the WMS index. Both indices are well-validated and can therefore be used by researchers interested in studying management across a wider range of countries and schools than was previously possible.

> 4Our approach follows the spirit of the re-casting of the original phone-based World Management Survey into the US Census Management and Organizational Practices Survey (MOPS) administered to the population of US manufacturing establishments as a self-reported questionnaire [Bloom et al., 2019a]. The MOPS has been replicated in a number of other countries. Its questions follow the WMS topics and look to measure similar practices, but with self-reported answers.

5
Policy Research Working Paper 5439

# **Abstract**

This paper examines the effect of the financial crisis on off-farm employment of China’s rural labor force. Using a national representative data set collected from across China, the paper finds that there was a substantial impact. By April 2009 the reduction in off-farm employment as a result of the crises was 6.8 percent of the rural labor force. Monthly earnings also declined.

However, while it is estimated that 49 million were laid-off between October 2008 and April 2009, half of them were re-hired in off-farm work by April 2009. By August 2009, less than 2 percent of the rural labor force was unemployed due to the crisis. The robust recovery appears to have helped avoid instability.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to study the effects of crisis related economic shocks on labor markets and household well-being. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jgiles@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
districts in 29 countries; ii) urbanization patterns from 1992 to 2008 in 1,158 cities. Their estimates show that climatic conditions do affect urbanization rates, with better conditions delaying urbanization and adverse conditions leading to faster urban population growth, but that these effects are confined to a subset of about 20%-25% of Sub-Saharan African districts.

In a study already mentioned above, <mark>Martinez Flores, Milusheva and Reichert</mark> (2021) leverage highfrequency (daily) migration data on the place of origin of migrants and the time of migration, collected from the International Organization for Migration in 17 West and Central African countries over the period 2018-2019, and estimate that droughts, as measured by soil moisture anomalies during the growing season, strongly reduce the number of international migrants. As they detect these effects only in middle-income areas but neither in rich nor poor ones, they conclude that climate-induced liquidity constraints and income losses are the key mediating channels.

Finally, in a recent working paper, Di Falco, Kis, and Viarengo (2022) exploit LSMS-ISA panel data 15
---|---|---|---|---|---|
||No|Yes|No|Yes|No|Yes|No|Yes|
|Female Employees|19.81|43.19|20.60|79.27|21.12|87.86|21.30|108.81|
|Female Production Workers|14.35|72.83|21.45|85.13|23.95|129.54|26.50|126.43|
|Female Non-Production Workers|5.30|18.48|6.20|25.86|7.66|28.18|7.90|35.07|

Source: Constructed by the authors using the WBES.

Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether.

Yet, it is important to look at women’s participation from a broader lens by taking into consideration, not only female workers, but whether the owner or the manager of the firm are females. These two measures can give a clearer picture of women’s empowerment as they are associated to more female power within the firm. Thus, Figure 1 compares firms that are part of GVCs whose owner or manager are women. Three remarks are worth mentioning. First, generally, the share of firms that are owned or managed by females is limited, compared to those owned or managed by males. Second, females that own a firm that is part of a GVC are higher than those who just manage it. This is closely related to the concept of empowerment as it captures the effect on women’s empowerment more than management given that the manager is, at the end of the day, an employee in the firm that takes orders from the