rafmacalaba/data-use-mentions-extended huggingface dataset:
Primarily activity is domestic work_**|0.7|9.2|5.1|
|**_Share of above who report non domestic secondary activity_**||||
|No|51.7|27.5|28.9|
|_Yes_|_48.3_|_72.5_|_71.1_|
|Total|100|100|100|

_Source: Author calculation based on the IHS 1992/93._ Even if screening is done well, the inclusion of inactivity options in employment status is problematic. This is evident in the 2005/6 data, where screening was included in the 7-day economic activity question but not 12 month employment one. The 12 month question was similar to those used in 1992/3, where only employment status was asked, with the inclusion of inactivity responses. As seen in Table 3, once again a sizable share of those reporting an inactivity response in the primary employment status had an economic activity as their secondary response. Not shown in the tables is that of this 10 percent of respondents found to be economically active by using secondary activity, _80 percent actually reported a primary economic activity in 7 day question, where inactivity responses were not included in the activity question_ . Again, those mismeasured mainly reported their activity type as “contributing family workers” in the 7 day recall. In addition to females working at home, the economic activity of students also tends to be undermeasured. Of the 20% of the respondents aged 14 -65 who reported being inactive full time students as their primary status in the 12 months recall period, 65% of these reported a undertaking secondary _economic_ activity in the same recall period (see Table 5).

15
One of the main weaknesses of the current approach regarding road investments is the fact that it is built on the strong assumption among policy makers that the existence of a road in good condition in rural areas will enable service providers to come and serve rural areas. In most cases, transport services economics is completely neglected, assuming that road condition is the main determinant of transport costs.

A notable exception was Hine and Rutter (2000), who, based on surveys in almost 100 villages in Ghana and Malawi at the end of the 1990s, found that “vehicle accessibility alone does not guarantee [...] transport service access”. Moreover, Lall (2009), which, in the case of Malawi, demonstrated, based on trucking surveys and computation of vehicle operating costs in SSA, that _both_ infrastructure quality and market structure of the trucking industry are important contributors to differences in transport costs. Lall (2009) points out costs due to poor feeder roads are exacerbated by low volumes of trade between rural locations and market centers. With empty backhauls and journeys covering small distances, only a few transport service providers enter the market, charging high prices to cover fixed costs and maximize markups.

Raballand et al. (2010) find in field surveys in Uganda, Cameroon and Burkina Faso that load consolidation at the local level decreases the need for a road accessible by truck to every farm and it decreases investment needs and increases value-added for farmers. From a costbenefit analysis, load consolidation (or agglomeration) is probably the most effective since it mainly reduces road public investment to the secondary network and enables decrease of transport costs due to increased predictability of volumes and strengthened competition between operators.

In the case of this paper, through a randomized evaluation, a passenger bus line was introduced between a rural cluster of villages and a town that serves as a regional trading hub. The study was implemented in rural Malawi, more than two hours by car from the nearest urban location. While most residents of the villages had been
Policy Research Working Paper 10583

# **Abstract**

Given increasing levels of displacement due to conflict and climate change, it is important to establish robust monitoring systems. This paper explores how remote sensing data, particularly geospatial data, can be leveraged to monitor displacement flows. It draws lessons from northeastern Afghanistan, namely the 2018 drought, which is considered one of the worst in decades. The analysis identifies displacement patterns by combining displacement data from the International Organization for Migration Displacement Tracking Matrix with nighttime lights. The results suggest that the cumulated displacement movements from 2018 to 2020 can be proxied by trends in nighttime light imagery. Settlements with higher net inflows of displaced persons between 2018 and 2020 have comparatively larger nighttime light growth. Allowing for nonlinearity suggests decreasing marginal returns of displacement on nighttime lights, as settlements showing the largest expansion of nighttime lights are those with the lowest displacement inflows. The model uses data on nighttime lights to predict whether a settlement was a net receiver of displacement flows during 2018–20 and correctly classifies 63.2 percent of the settlements as net inflow or net outflow. This study provides a proof of concept to test whether population displacements can be proxied using geospatial data trained on administrative records in a data-scarce environment, where real-time insights can inform humanitarian assistance. This work was done before the political crisis of August 2021.

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/prwp. The authors may be contacted at adahmaniscuitti@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
|0.4026|
|Observations|668639|668285|668285|668639|668285|668285|668638|668284|668284|
||||||Female|||||
|CatchmentArea X Post|0.0379**|0.0409**|0.0345**|0.0243|0.0254*|0.0201|0.0434**|0.0464***|0.0397**|
||(0.0171)|(0.0160)|(0.0158)|(0.0151)|(0.0148)|(0.0148)|(0.0181)|(0.0170)|(0.0167)|
||[ 0.042 ]|[ 0.023 ]|[ 0.043 ]|[ 0.124 ]|[ 0.103 ]|[ 0.196 ]|[ 0.031 ]|[ 0.015 ]|[ 0.032 ]|
|R-squared|0.3983|0.4432|0.4432|0.3888|0.4299|0.4300|0.3712|0.4187|0.4187|
|Observations|818714|818396|818396|818717|818399|818399|818714|818396|818396|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|
bill shares (employment shares) are de fi ned

> 23A detailed de fi nition of these fi ve types of workers is available from the authors upon request. In the Chinese survey, some of the worker types have different labels (professionals are called engineering and technical personnel, skilled production workers are called basic production workers and unskilled production workers are called auxiliary production workers) and some worker types are actually different but can be mapped into these fi ve worker types (service personnel and other employees are mapped into other nonproduction workers).

> 24In this case, unskilled workers are production workers de fi ned as the sum of skilled production workers and unskilled production workers.

> 25In this case, unskilled workers are de fi ned to be the sum of skilled production workers, unskilled production workers and other nonproduction workers.

> 26An example of such workers would be janitors.

> 27In this case, unskilled workers are de fi ned as workers that have completed secondary education or less.

11
PoFcy RESEARCH WORKINC PAPER 1 733

# Summary findings

Using a specially designed survey of Japanese firms planning investmenrs in Asia, Kinoshira and Mody emphasize the importance of privately held information in making for¢ign investment decisions.

> Information on operating conditions hased oTn direct experience in a country is likely to be rlte mnost credible Information, but for investors new to a c ountry, information inferred from observing others investing in that country may he more influential. Initially, in fact,

> observing the actions of comppetitors seenms to lead to cascading investments in that -ountry, apparenitly through herd behavior (as in China anid Vietnam). Countries whiich do not draw a critical mass of investors are in danger of being bypassed for significint perto ls. Publicly available information is iniip(Jrtatlit in helping

> shape "average" perceptions about a couLntry, leading

potential investors to view particular locations favorably. Privatclv held information complements that public inforination, and ac-oulits for iniportant variations in rive.tinier plans.

Sulj¢jective perceptions about policy on foreign direct investinenit are also important.

- Pol cv designed co attract invcstors for exaimiple, .speci. _, zones_ for foreign investors -have been *.icce tful in nmanv instances, cspecially **in** East Asia, but have ilso beell a waste of scarce investmuent resources when not appropriately planned. And such policy is of lirtle alne in artracting those already investing in a country, or those who alruady perceive rivals to be active rhere

This paper - a product of the East Asia and Pacific Regional Of tice -- is part of a larger effort in the department to study trends in foreign investment in the region. Copies of the paper are av **1ilable** free ftrm the World Bank, 181 8 H Street NW, Washington, DC 20433. Please contact Rafaela Reff, room Q5- I3 9, n.luphonc 202 473-48 15, fax 202-477-0218, Internct address rreff(&worldbank.org. February 1997. (32 pages) _The Policy Researcbh WZorking
|0.43|0.65***|0.36|0.31***|
|_Individual_|||||||||||
|Literate|0.58|0.54***|0.66|0.53***|0.53|0.39***|0.59|0.54|0.67|0.69**|
|Married|0.86|0.75***|0.89|0.87***|0.91|0.9|0.82|0.81|0.88|0.89|
|Divorced or Separated|0.07|0.11***|0.02|0.03***|0.01|0.01|0.05|0.06|0.02|0.02|
|Widowed|0.06|0.05|0.06|0.08***|0.03|0.03|0.1|0.11|0.05|0.06|
|Single|0.01|0.09***|0.04|0.02***|0.06|0.06*|0.03|0.02|0.04|0.04*|

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

Overall, while statistically significant differences do exist in the household composition of forcibly displaced and non-displaced households, the magnitude of these differences is mostly minor. The fact that the household composition is similar
Where do poor children live? Child poverty rates, like those for adults, are much high in rural areas. Nearly one out of three children in rural areas is extremely poor, and over 81 percent of poor children live in rural areas. Nearly 88 percent of poor children are located in Sub-Saharan Africa or South Asia, and child poverty rates tend to be much higher in Sub-Saharan Africa than in the rest of the developing world. This illustrates the magnitude of the challenge of ensuring that children in rural areas, and in Africa and South Asia, benefit from valuable early childhood investments in human capital.

A fundamental question involves the robustness of these estimates of child poverty with respect to the use of alternative equivalence scales. The new $1.90 per day poverty line, like the $1.25 line that it is based on, is a per capita line. It is derived from the national poverty lines of 15 poor countries, which in turn are each developed using per capita consumption as the welfare measure. Following Ravallion (2015), we apply alternative equivalence scales to both the consumption aggregate and the poverty line, for a typical household in the neighbourhood of the poverty line. After making this adjustment, child poverty rates are quite robust with respect to the choice of two-parameter equivalence scale. Child poverty rates do not dip below 17 percent until children are given one-fifth of the weight of adults to account for their lower expenditure needs, which is not realistic. In short, higher child poverty rates are robust to any sensible two-parameter equivalence scale.

The analysis leaves open three main areas for future work. First, we plan to explore the characteristics of poor children in greater detail, and analyse the variation across child poverty rates across countries and, wherever possible, to also consider non-monetary aspects of child poverty. Second, we plan to explore in detail the relationship between welfare and school attendance. This will help shed light on whether there is a particular level of welfare at which school attendance rises sharply,
|
|Business formation|To measure entrepreneurial activity, annual data was collected from<br>143 company registrars on the number of newly registered firms. New<br>business entry is defined as the number of newly registered<br>corporations per 1000 working-age people (those ages 15–64). As in<br>the World Bank’s annual Doing Business report, the units of<br>measurement are private, formal sector companies with limited<br>liability._Source:_World Bank’s Entrepreneurship Survey and database<br>(econ.worldbank.org/research/entrepreneurship).|
|Doing Business index|Aggregate Doing Business distance to frontier (DTF) score. An<br>economy’s distance to frontier is reflected on a scale from 0 to 100,<br>where 0 represents the lowest performance and 100 represents the|
||frontier._Source_: Doing Business,www.doingbusiness.org|
|Starting a Business|Starting a Business topic distance to frontier (DTF) score._Source_: Doing<br>Business,www.doingbusiness.org|
|Acquiring Licenses|Dealing with Construction Permits topic distance to frontier (DTF)<br>score._Source_: Doing Business,www.doingbusiness.org|
|Getting Credit|Getting Credit topic distance to frontier (DTF) score._Source_: Doing<br>Business,www.doingbusiness.org|
|Enforcing Contracts|Enforcing Contracts topic distance to frontier (DTF) score._Source_:<br>Doing Business,www.doingbusiness.org|
|Income per capita|One year lag of natural log of GDP per capita (current USD)._Source:_<br>WDI, World Bank|
|PolChange|Dummy equal to 1 if an alternation of political parties or coalitions<br>took place 12 months prior to the start of the_Doing Business_reforms|

4
around 51 percent of the variance between student performance in mathematics and science.

School safety and disciplinary climate however may vary with urbanization _._ School safety tends to be lower in urban and densely populated areas and higher in remote rural areas (Figure 3). Once country differences in urbanization level and socioeconomic status are accounted for, the relation between population density and school safety is quite continuous in MENA. All other things equal, safety tends to deteriorate as population density increases. On average, school safety tends to be around 25 percent of a standard deviation better in remote rural schools than in schools located in densely populated areas. This suggests that the level of urbanization plays a substantial role. Additionally, analysis of the classroom disciplinary climate index confirms that disciplinary climate is also on average better in rural areas than in large cities.

Gender composition of the school matters for disciplinary climate, as climate tends to deteriorate as the share of boys increases. On average, as the share of girls rises from 0 to 100 percent, the disciplinary climate measured at the school level increases by 0.25 standard deviation. The impact of gender composition on disciplinary climate is also positive.

> 5 The school safety index went down by 0.7 standard deviation.

> 6 See the appendix for details.

6
>**sample**|
|---|---|---|---|---|---|
|Ethiopia|Skills Profile<br>Survey<br>(2017)|Multi-stage<br>stratified random<br>sample|Refugee camps and<br>proximity in Tigray<br>Afar,<br>Gambella,<br>Benishangul<br>Gumuz,<br>and Somali regions|Refugees<br>from<br>Eritrean,<br>South<br>Sudanese,<br>Sudanese,<br>and<br>Somali and hosts|26,517<br>(96.5%)|
|Northeast<br>Nigeria|IDP Survey<br>(2018)|Multi-stage<br>stratified random<br>sample|Six Northeastern states<br>(Adamawa,<br>Bauchi,<br>Borno, Gombe, Taraba,<br>and Yobe)|IDPs and host<br>communities|17,543<br>(97.7%)|

10
# **6 Monitoring by Foreign Investors**

Based on results in Section 5, the entry of foreign investors into domestic stock market triggers an efficiency-enhancing structural change by re-balancing countries’ export portfolios towards their comparative advantage. A possible cause behind these findings might lie in the monitoring effects of foreign investors in domestic firms. An influential strand of literature argues that foreign investors are more effective as external monitors compared to domestic investors who often feel loyal to the local management (e.g., Gillan and Starks 2003; Ferreira and Matos 2008; Aggarwal et al. 2011; Bena et al. 2017; Luong et al. 2017). If that is true, then stricter monitoring by foreign investors should push the exporting managers to abandon products that are far away from the comparative advantage of the domestic economy. This section provides some preliminary evidence in accordance with this interpretation.<sup>27</sup> If our findings are indeed driven by the superior monitoring skills of foreign investors, then the results should become stronger in a legal environment providing sufficient protection to the rights of investors in general and of foreign investors in particular. Arguably, foreign investors can fully employ their monitoring abilities only if they can rely on a robust legal framework protecting their rights vis-vis corporate insiders and local government. Table 8 examines this hypothesis, based on a composite index Legal System and Property Rights from the Economic Freedom Database. The index captures various legal dimensions relevant for investor protection like integrity of legal system, legal enforcement of contracts, protection of property rights, impartiality of courts, judicial independence, etc., with higher values of the index corresponding to higher levels of investor protection. The index varies both across countries and over time. Due to possible endogeneity concerns, we use the values measured in 1980, which is the first year of our sample.

Column (1) of Table 8 runs our preferred specification for all countries from column (3) of Table 3 on the subsample of countries for which we have data
|0.42|0.25|0.43|0.59|0.49|0.63|0.48|0.61|0.49|
|High school|0.09|0.28|0.08|0.26|0.08|0.27|0.41|0.49|0.41|0.49|0.41|0.49|
|Share classes in school with full-time day (7 or more hours)|0.02|0.12|0.03|0.15|0.02|0.14|0.01|0.08|0.01|0.08|0.01|0.08|
|Total duration of school day (hours)|4.31|0.64|4.34|0.72|4.32|0.68|4.30|0.49|4.30|0.50|4.30|0.49|
|Numberofobservations|6|11|385||996|||546|3|19||865|

_Sources:_ MEC/FNDE, INEP, _Prova Brasil,_ and School Census.
.0006)|
|rest|0.24***<br>(0.0078)|
|total employment|0.16*<br>(0.0794)|
|Between seasonally adjusted GDP and<br>self-employment|0.14<br>(0.1302)|
|rest|0.04<br>(0.6353)|

**Note:** *** p<0.01, ** p<0.05, * p<0.10. Number of observations (n=123). **Source:** South Asia Employment Dataset.

Each of the resulting figures is an estimate of the elasticity of employment growth to economic growth, or the percentage change in employment for every percentage point of GDP growth. We summarize the distribution of these elasticities for South Asian countries in Table 4. On average, one percentage point of economic growth has led to an increase of employment by 0.34 percent. When only considering the data points furthest away from each other, the elasticity decreases to 0.19 as in the long-run rising GDP per capita negatively affects the employment rate (World Bank 2018a). Using these long-run estimates, Bangladesh has generated 110,000 jobs per percentage point of growth, India 750,000, Pakistan 200,000 and Sri Lanka 9,000.

16
manufacturers' suggested retail price. The model simulates excise taxes based on information about the quantities of excisable goods consumed by households (if quantities are not reported, they are recovered from expenditures with <u>assumptions about the average unit value; for example, price for 1 liter of petrol).</u> Notes: (1) Typically, the eligibility for social allowances is defined using incomes reported for the 3, 6 or 12 months preceding the date of application. However, in the survey data, the reference period is 1 month preceding the survey. In simulations we assume that the amount of income reported is received at the same rate throughout the year.

(2) There are also rules in the policy spine not shown above because they do not define programs. Rather, they define objects, such as tax units, income lists, unreported incomes, subsistence minimum level, etc., that are used by the actual tax/benefit rules.

**Table A-2. RUSMOD input database, 2017**

|RUSMOD database|RF_2016_a1|
|---|---|
|Original name<br>Provider|Russian Longitudinal Monitoring Survey (RLMS-HSE)<br>National Research University - Higher School of Economics|
|Year of collection|2016|
|Period of collection|October-December 2016|
|Income reference period|Typically income and expenditure for the month preceding the survey, for some types<br>of expenditures - 3 months preceding the survey|
|Sampling|A three-stage stratified clustered probability sample of dwellings|
|Unit of assessment|Individual and household (people living together and sharing income and<br>expenditures)|
|Coverage|Permanent residents, people living in institutions are excluded|
|Response rate for household grid|80% (60% in Moscow and St-Petersburg)|
|Final sample used in the model||
|Individuals|12,554|
|Households|4,849|
|Population with grossing-up weight*||
|Individuals|
American countries were not surveyed in 2006. The main benefit of using the panel data is that it allows us to control firm fixed-effects such as the entrepreneurial ability of the managers. These characteristics could make a firm a target of bribery solicitations as well as affect sales growth. Thus, the panel data has the advantage over the cross-sectional data in handling the concerns on the endogeneity of the bribe measures. However, the size of the panel is quite small (1,724 observations). For this sample, we apply the first difference estimation given as In the estimation with the panel data we don’t exclude firms that are in a country-sectorlocation cell with less than 30 observations as we do not apply the IV method. Results using the bribery indicator (with whole sample) are presented in Table 14. The sample is not restricted to sub-sample of firms with business transactions because such a restriction significantly decreases the sample size. In the second column of the table, we find that bribed firms have sales growth that is 30 percentage points lower than those who were not solicited for bribes and the impact of bribery on small firms is more severe. In the panel results, the interaction term of bribery with age is no longer significant.

# **5. Conclusion**

A limited number of studies have examined the relationship between bribery and firm evolution. In this study, we contribute to this literature by using firm-level data from 29 developing countries in the LAC region. Our analysis is novel in a few dimensions. Micro-level data on corruption are scarce. We use data from Enterprise surveys which apply the same methodology to collect data from a large set of countries. This allows us to combine data for different countries. Second, we study the differential effects of corruption on growth. We assess the impact of bribery on sales growth by focusing on how bribery affects firms differently based on their sales and age.

22