rafmacalaba/data-use-mentions-extended huggingface dataset:
variables developed by Coulombe and MacKay (1995) and used by other analysts of these data. The poverty line is de-fined as 132,300 cedis which amounts to $25 per month per person in 1992 prices. The GLSS surveys, which cover about 3000-4000 households, are nationally representative mu,lti-purpose surveys conducted over a period of one year.<sup>4</sup> For Uganda, we use twvo household surveys, the Integrated Household Survey from 1992 (IHS 1992) and the fourth Monitoring Survey from 1996 (MS 1996). Like the GLSS, the IHS 1992 survey was modeled after the LSMS to measure welfare. The MS 1996, like the other annual mnonitoring surveys implemented after 1992, was designed to collect vital welfare data such as consumption and labor data. We use the regional poverty lines and mean per capita expenditure variables developed by Appleton (1999). The resulting weighted average national poverty line is 16443 Ugandan shillings per adult equivalent per month (in 1992 prices) or about $34 per person per month.

Since this paper exploires the gender dimensions of non-farm activity, we chose to make maximum use of individual level data wherever available. We used all of the information available for economic activity, main and secondary occupations (and tertiary where given), since many people-especially women-participate in more than one sector. In fact, most of the interesting information about non-farm participation comes from the analysis of the different combinations of activities. We also use imputed individual level incomes for Ghana that shed light on the different gender dimensions of the labor market. We estimate individual incomes by combining existing salary data with a proportional share of household income based on the individual's reported participation

> 4 GLSS 1 had a 14 day recall period while GLSS3 had a much smaller 3 day recall period. Since longer recall periods are associated with recall error the expenditure figures had to be corrected using a correction factor of 2.7 for each additional day of longer recall period in order to avoid unwanted bias in the
Policy Research Working Paper 8958

# **Abstract**

Understanding how land prices are determined is of particular importance for policy makers; however, there is little evidence in African countries, which are currently experiencing rapid urbanization. The paper examines the relationship between land prices and locational characteristics using data from Antananarivo, the capital of Madagascar. It is found that the land value gradients are relatively steep, indicating that the land and housing prices tend to overshoot in the middle of the city, pushing the poor away from the city to suburban areas. It is also found that access to transport infrastructure and services, such as minibuses, is an important determinant of land value. Not only transport connectivity, but also other factors, such as proximity to amenities and administrative centers, are found to be important. Better land management and urban transport policies are called for to promote these aspects in the city.

This paper is a product of the Transport and Digital Development 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 author may be contacted at aiimi@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
Table 3

|**Leverage Ratio of Private Infrastructure**<br>**Projects**<br>**(by sector)**|
|---|
|Sector<br>Mean<br>Sample Size<br>(standard deviation)|
|Power<br>73.07<br>28<br>(8.15)|
|Roads<br>63.07<br>15<br>(15.43)|
|Transport<br>77.86<br>7<br>(8.09)|
|Telecoms<br>61.25<br>4<br>(2.50)|
|Waste/Water<br>75.00<br>4<br>(12.25)|
|Gas<br>67.00<br>1|

* Percentage ratio of total debt (book value) to a project's investment cost

Source: Authors' estimates based on a sample of infrastructure projects closed between 1994 and 1996 (see below).

# **III. Analytical Framework**

To motivate the discussion of determinants of the risk premia on private foreign currency loans, we begin with a highly simplified model of bank lending with exogenously specified probability of country default, or financial distress. The key factor that distinguishes a foreign loan from a domestic one is the presence of country risk. The importance of country risk in internationally-funded infrastructure projects is highlighted by the fact that even if a project is commercially viable, its ability to service its foreign debt or equity depends on broader governmental policies regarding capital mobility and currency convertibility, which are beyond the control of the project entity. Technically, consider the required return to creditors, or cost of debt capital, from a particular project in a given country. This required rate of return is denoted by i and can be expressed as: _i= r_ + _s,_ where r is
- Islam, M.R., Li, T., Mahata, K., Khanal, N., Werden, B., Giordano, M.R., Praveen Puppala, S., Dhital, N.B., Gurung, A., Saikawa, E., Panday, A.K., Yokelson, R.J., DeCarlo, P.F., Stone, E.A., 2022. Wintertime Air Quality across the Kathmandu Valley, Nepal: Concentration, Composition, and Sources of Fine and Coarse Particulate Matter. ACS Earth and Space Chemistry 6, 2955-2971.

- Jayarathne, T., Stockwell, C.E., Bhave, P.V., Praveen, P.S., Rathnayake, C.M., Islam, M.R., Panday, A.K., Adhikari, S., Maharjan, R., Goetz, J.D., DeCarlo, P.F., Saikawa, E., Yokelson, R.J., Stone, E.A., 2018. Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): emissions of particulate matter from wood- and dung-fueled cooking fires, garbage and crop residue burning, brick kilns, and other sources. Atmos. Chem. Phys. 18, 2259-2286.

- Kamyotra, J.S., 2015. Brick Kilns in India. Presentation. Central Pollution Control Board (CPCB), New Delhi.

- Kaushik, R., Khaliq, F., Subramaneyaan, M., Ahmed, R.S., 2012. Pulmonary dysfunctions, oxidative stress and DNA damage in brick kiln workers. Human & Experimental Toxicology 31, 1083-1091.

- Khisroon, M., Khan, A., Imran, M., Zaidi, F., Ahmadullah, Fatima, S.H., 2018. Biomonitoring of DNA damage in individuals exposed to brick kiln pollution from Peshawar, Khyber Pakhtunkhwa, Pakistan.
# **Table S20. Concerns about health and finance across consumption quintile**

||Concerned about<br>household health|Concerned about<br>household finances|
|---|---|---|
|Ethiopia|-0.219***<br>(0.019)|-0.150***<br>(0.016)|
|Nigeria|-0.141***|-0.032***|
||(0.017)|(0.012)|
|Uganda|-0.217***|-0.133***|
||(0.017)|(0.014)|
|Quintile 2|-0.004|-0.019|
||(0.031)|(0.027)|
|Quintile 3|-0.064*|-0.012|
||(0.032)|(0.026)|
|Quintile 4|-0.059**|0.004|
||(0.030)|(0.025)|
|Quintile 5|-0.094***|-0.006|
||(0.028)|(0.023)|
|Ethiopia-Nigeria|0.000|0.000|
|Ethiopia-Uganda|0.929|0.351|
|Nigeria-Uganda|0.000|0.000|
|Observations|8,846|8,847|
|R-squared|0.024|0.032|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent answered “yes” to the question of whether they were concerned that a household member may fall ill with COVID-19 or that the household would experience financial distress due to COVID-19. The binary variable is regressed on indicators for each country,
completed high school. When information is available on academic abilities (for instance grade point averages at upper secondary school), socio-economic background, tuition fee levels, college premium, and financial support, and the outcome of their enrollment decision (enrollment or no enrollment) we could estimate enrollment elasticities with regard to tuition fee levels and financial support. Unfortunately, the SOFES database and the questionnaire held in the summer of 2003 only provide information on people who already are enrolled in a higher education program. In order to get an idea of the impact of financial support on student enrollment, we explore information from the Mexican household survey ENIGH 2000 in this section.

12
|Afrobarometer|
|**Access to Basic Public Services**|||||||||
|Share of households without access to adequate sanitation<br>Share of households with access to improved water|130<br>129|X<br>X|X<br>X|X<br>X||||GMD<br>GMD|
|<br>Share of households with access to electricity<br>|93<br>|X<br>|X<br>|X<br>||||GMD<br>|
|Internet connection at home|45|X|X|X||||GMD|
|**Access to Human Capital Services**|||||||||
|Primary enrollment rate<br>ll|100<br>|X<br>|X<br>|X<br>||||GMD<br>|
|Secondary enroment rate<br>Share of households with health insurance|104<br>24|X<br>X|X<br>X|X<br>X||||GMD<br>GMD|
|Share of households with mobile<br>Share of households with computer|93<br>87|X<br>X<br>**Resilien**|X<br>X<br>**ce**|X<br>X||||GMD<br>GMD|
|<br>Share of households with washing machine<br>Share of households with car or motorcycle|57<br>67/57|X<br>X|X<br>X|X<br>X||||GMD<br>GMD|
|<br>Received domestic remittances in the past year|34|X|X|X|X|X
br>**Off**|**cial De**|**velopment Assistance from China**|**30**|
|4.1|Chine|se aid is small compared to commercial activities . . . . . . . . . . . . . . . . .|30|
||<br>4.1.1|<br>Kenya may rely more on Chinese aid because of volatile aid fows<br>. . . . . .|31|
|4.2|Most|<br>Chinese fnancing does not qualify as ODA . . . . . . . . . . . . . . . . . . . . .|32|
||4.2.1|China loans the most to ministries of energy and petroleum and transport<br>and||
|||infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|32|
||4.2.2|Chinese stands out in education aid . . . . . . . . . . . . . . . . . . . . . . . .|33|
|4.3|Lack o|f quality Chinese aid data<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|34|
||4.3.1|Drawbacks of existing Chinese aid data: Media based data collection is prob-<br>lematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|34|

2
<br>UN<br>population<br>statistics;<br>UN<br>175<br>countries,|
||women<br>population projections<br>1960-2095|
|Employment|Number<br>Penn World Table<br>181<br>countries,<br>1950-2019|
|Urban population|Share of total population<br>World Development Indicators<br>194<br>countries,<br>1960-2020|
|R&D spending|In percent of GDP<br>World Development Indicators<br>144<br>countries,<br>1996-2019|
|Consumer price inflatio|n Percent<br>Haver Analytics<br>93<br>countries,<br>1980Q1-2021Q4|
|Inflation expectations|Percent<br>Consensus Economics<br>74<br>countries,<br>|
||1980Q1-2021Q4|
|Unemployment rate|Percent of labor force<br>Haver Analytics<br>66<br>countries,<br>|
||1980Q1-2021Q4|
|Capacity utilization rate|<br>Percent of capacity<br>Haver Analytics<br>31<br>countries,<br>1980Q1-2021Q4|
|Import price inflation|Percent<br>Haver Analytics<br>74<br>countries,<br>1980Q1-2021Q4|
|Private credit growth|Percentage points of GDP<br>Haver Analytics<br>57<br>countries,<br>1980Q1-2021Q4|
|Average<br>commodi|ty<br>Index<br>World Bank; Federal Reserve Bank<br>93<br>countries,|
|export price|of St. Louis; UN Comtrade<br>1980Q1-2021Q4|
|Monetary policy rates|Percent<br>Haver Analytics<br>80<br>countries,|
||<br> <br>1980Q1-2021Q4|
|House prices|46<br>Bank for International Settlements 55<br>countries,
and Pavoni (2020)). We expect positive effects of the policy on the labor force participation rate of mothers of children aged six years old.

To estimate the impact of the reform, we apply a difference-in-difference estimation strategy and use mothers of three- to four-year-old children as our control group. We rely on data from the European Labor Force Survey (EU-LFS), which contains information on labor market outcomes and allows us to identify the age of the youngest child in each household as well as the mother of each child in each household. We only have access to data from 2010 to 2019, which limits our analysis of parallel trends.

We find that the 2012 reform led to an increase in the maternal labor supply of affected children by 2.6 to 4.1 percentage points. Results from a triple difference-in-difference estimation strategy that relies on information on children’s average participation in pre(primary) classes are less stable than our difference-in-difference estimators, probably driven by low sample sizes.

Women who previously experienced more severe restrictions to labor market opportunities, by living in rural areas, caring for additional children, being self-employed, or having greater economic safety due to the presence of a spouse or employed member in the household, report larger effects. Women living with an elderly person experience lower effects, but the difference is insignificant.

We also show that the probability of working part-time increased among women affected by the reform. This result is not surprising given that preparatory classes only took place in the morning. Consequently, the reform only represents a partial substitution of the care responsibilities endured by the primary caregiver. Nevertheless, the average number of hours usually worked during the week slightly increased. So did the average gross employee cash or near cash income. Future research should disentangle if these income increases are mainly driven by mothers entering the workforce, or if there was also a parallel increase in existing earnings.

Romania is an interesting setting for the underlying question, given that gender gaps in 2
this magnitude, the dramatic reduction in poverty would be understandable. Given the increase in poverty in urban areas, and return migration from the urban areas to the countryside, the labor constraints may slacken and real wages fall over the next few years. However, one should be careful in interpreting the results of the socioeconomic breakdown in the survey data, as we do not know what percentage of household income is made up of wage labor, and whether wage incomes increased in the households classified as wage laborers. Moreover, given the various categories of hired labor that exist, it is not clear that the surveys do an adequate job of identifying all the various forms of hired labor and coding them correctly. So it is entirely possible that the observed decline in poverty in this group of households may have little or nothing to do with labor earnings per se.

There was a substantial increase in poverty among public sector employees, as expected given that real public sector wage bill declined in real terms. Following the devaluation in January 1994, the government granted a 10 percent increase in wages and salaries across the board, although fringe benefits remained unchanged. Thus, the overall increase in personnel remuneration amounted to about 8 percent. The number of employees remained essentially unchanged. The rate of inflation between the two surveys was 48 percent for Abidjan (the pre-devaluation survey was conducted April-May 1992) and 43 percent for the rest of the country (the pre-devaluation survey was conducted between JuneNovember 1993). Thus remuneration of Abidjan civil servants fell by 27 percent in real terms, while remuneration of civil servants outside Abidjan fell by 24 percent. The survey data show that household expenditure per capita of Abidjan public employee households fell by 15 percent, while expenditure per capita of public sector employee households in Other Cities fell by 24 percent. Thus, it seems that public sector employee households in Abidjan were better placed to draw on other sources of income or savings to mitigate the decline in their public sector salaries than public sector employees outside Abidjan.

Tlhe outcome for
0.355*|0.351*|0.336*|0.335**|
||||(0.181)|(0.182)|(0.182)|(0.162)|
|Log average wage|||0.0151|0.0160|0.0128|0.0147|
||||(0.0201)|(0.0201)|(0.0203)|(0.0198)|
|Log tobacco tax||||0.0236|0.0232|0.0196|
|||||(0.0204)|(0.0205)|(0.0191)|
|Log SNAP average gross income|||||-0.101|-0.102|
||||||(0.0872)|(0.0897)|
|Log SNAP average rent|||||0.0226*|0.0220*|
||||||(0.0119)|(0.0126)|
|Observations|382560|382560|382560|382560|382560|382524|
|R-squared|0.979|0.986|0.987|0.987|0.987|0.988|
|Prob _>_ F|0.115|0.000|0.000|0.000|0.000|0.000|
|Number of units|7970|7970|7970|7970|7970|7970|
|Number of clusters|48|48|48|48|48|48|
|Energy controls||||||X|

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Store and period
inspections as do formal sector firms. They report more frequent power cuts but are no more likely to have a generator than formal sector firms.

# _Comparison of rankings of constraints by informal and formal sector firms_

In terms of rankings (Table 19), we note first that political instability was absent from all of the India surveys. Electricity, corruption and tax administration were the three highest ranked constraints among formal sector firms (“all” and “small”). The ranking of constraints by informal sector firms was quite different. Electricity was in the middle of the ranking – so although informal firms reported more power cuts and there was the same prevalence of generators, their lower ranking of electricity suggests they were typically much less dependent on it in their production process than were formal sector firms.

The top-ranked constraints for informal sector firms were transport, business licensing, inadequately educated labor force and access to land. Access to land is ranked much lower by formal sector firms – as are business licensing and transport, and to a lesser extent access to educated labor. It seems that informal firms answering the question about the obstacles to the growth of their firm are reflecting on the core factors of production that limit them attaining scale and perhaps making it worthwhile attaining “formal” status.

# **11.  Conclusion**

We have used firm-level data from surveys of firms in the South Asia Region to assess the constraints presented to firms by shortcomings in the provision of public inputs. As predicted by the modeling framework, better performing firms, and notably those that are job-creators, are more constrained by these shortcomings than are other firms, and are more likely to be engaged in activity to mitigate these shortcomings. We also find that characteristics associated with high productivity, such as export activity and firm size, are also associated with higher reported constraints. We also find significant variation across countries in terms of which firm characteristics are associated with constraints originating with which public inputs, 38
, and no health conditions applied to others. Both preventive and curative visits fulfilled the health clinic visit conditions of the program, though visits had to be to a public facility (either a dispensary, health center, or hospital). There were no further restrictions on the timing of visits, nor on the services to be received. Children aged 7–15 had to enroll in school and maintain an 80 percent attendance record.

> 5We lack administrative data on compliance with conditions. However, in each follow-up survey, we asked: “[For your last transfer payment,] did you receive less money than you usually get?” and “What do you think was the reason?” While one may hesitate to admit to non-compliance (for fear of sanction) and while this cannot tell us how many households had at least one payment reduced (it only tells us about the last), this gives some indication of compliance levels. At midline and endline respectively, 1.9 and 3.0 percent of treatment households reported receiving less than usual for a reason related to not meeting conditions.

> 6As our endline survey was carried out during August–October 2012, we define compliance with clinic visit conditions at endline for 2–5 year olds as having two or more clinic visits in the last year.

7
Until now, we have mainly considered the issue of the choice of the time window by simply varying the window to exposure, using _t_<sup>_′_</sup> _∈_ 1 _,_ 3 _,_ 6. However, this treatment may not be sufficient and, in fact, obfuscate cumulative effects. Since a 3-month window is inclusive, the 1-month window, and the 6-month window is inclusive of both, the widening of time windows can increase the likelihood that a longer time window is, in fact, inclusive of exposure to more events. Since we have GPS-linked ACLED data over the full period before each GHS round, this conjecture can be tested. Specifically, in the full sample (excluding Borno) the likelihood that any individual was exposed to a herder-related violent event is a minuscule 0 _._ 8 percent. However, conditional on having been exposed to a violent event in the six months prior (that is, violence exposure in _t −_ 2 _, ...,_ 6, outside of _t −_ 1), the likelihood of exposure to violence in the last month increases to 8 _._ 2 percent, indicating a factor increase of 13.6x.<sup>33</sup> That is, very recent violence exposure is much higher in areas with other, _recent_ exposure.

If individuals change their labor supply decisions based upon their expectation of violence, then a reasonable assumption would be that continued exposure to such violence would elicit different reactions than among those for individuals who were not recently exposed to some herder-related violent event. To consider these possibilities, we amend equation (1) by adding an interaction term for exposure to another, recent event. Specifically, we fix the time window of our treatment to one month ( _t_<sup>_′_</sup> = 1), interacted with a dummy variable taking the value of one if there was other, recent exposure in the five months prior
nowcasting but we include all of them here – as we do with WDI and WEO – to remain agnostic about which ones are relevant and which ones are not.

For all of the variables from the three sources above we also calculate annualized growth rates of the variables between two household surveys for a country. For all variables that are expressed as percentages, rates, or indices, we also calculate the annualized change between two household surveys for a country.

The only variables we remove from what is described above are (1) variables with more than 90% missing information in 2020, the nearcasting year, (2) variables with more than 90% missing information for country-years with poverty estimates, and (3) variables that are not comparable between countries, such as variables expressed in local currency units. The first two criteria help speed up the models by removing variables with too little information to be relevant. The latter removes variables where exploiting cross-country variation is not meaningful.

Depending on which month of the year this exercise is carried out, the removal of variables with more than 90% in the nearcasting year removes a large fraction of the WDI. The reason is that early in the year, most variables do not yet have information for the prior year, meaning the information used for nearcasting is not much better than the information that can be used for nowcasting. When conducted in July (as the current exercise is), only about 300 WDI variables meet that criterion. All in all, we are left with a bit more than 1,000 features across the various data sources.

Not all poverty trends within countries are comparable over time due to changes in the survey methodology or the welfare aggregate. This matters for our predictions of in a variable. Even if we knew the exact causes changes/growth target of poverty in a country, if welfare aggregates are not comparable, then we would not be able to predict the changes in poverty between two surveys. Though this also makes it more difficult to predict