rafmacalaba/data-use-mentions-extended huggingface dataset:
(Truth and Reconciliation Commission 2004). As indicated, the effects of the war were concentrated in the eastern and southern districts, where a higher percentage of households were made refugees or had houses that burned down during the conflict. Over time, however, the war spread across the country, forcing people to abandon their communities (the Western peninsula was the only area not affected by this massive internal displacement).

During the war, the Revolutionary United Front (RUF) successfully targeted traditional authorities (Paramount Chiefs) as well as members of their households (Bellows and Miguel, 2009; Keen, 2005; Richards, 1996). Bellows and Miguel (2009) find that traditional authority households are statistically more likely to have experienced violence during the war. Thus, we include a control for whether respondents are a member of a ‘ruling house’.

# **NGO Service Provision**

Although NGOs provide a large portion of essential goods and services in Sierra Leone, there is no comprehensive data of the locations of NGO and non-state actor projects. The only measure of non-state actor service provision that is available is a question on the NPS that asks community leaders whether an “NGO (not government, Local Council, or MP) has done any projects since the end of the war (Feb 2002) that this community benefits from?” We aggregate the number of NGO projects that the community leaders believe have taken place to the enumeration area. Albeit imperfect, the inclusion of this measure allows us to 13
# **References**

- Abel, M. (2019). Long-run effects of forced resettlement: Evidence from apartheid south africa. _The Journal of Economic History 79_ (4), 915–953.

- Abramitzky, R. and L. Boustan (2017). Immigration in american economic history. _Journal of Economic Literature 55_ (4), 1311–45.

- Abramitzky, R. and L. Boustan (2022). _Streets of gold: America’s untold story of immigrant success_ . Hachette UK.

- Abramitzky, R., L. Boustan, E. J ́acome, and S. P ́erez (2021). Intergenerational mobility of immigrants in the united states over two centuries. _American Economic Review 111_ (2), 580– 608.

- Abramitzky, R., L. P. Boustan, and K. Eriksson (2014). A nation of immigrants: Assimilation and economic outcomes in the age of mass migration. _Journal of Political Economy 122_ (3), 467–506.

- Alesina, A. and S. Stantcheva (2020). Diversity, immigration, and redistribution. _AEA Papers and Proceedings 110_ , 329–34.

- Altindaˇg, O. and S. D. O’Connell (2023). The short-lived effects of unconditional cash transfers to refugees. _Journal of Development Economics 160_ , 102942.

- Amuedo-Dorantes, C. and F. Antman (2017). Schooling and labor market effects of temporary authorization: Evidence from daca. _Journal of Population Economics 30_ (1), 339–373.

- Amuedo-Dorantes, C. and C. Bansak (2011). The impact of amnesty on labor market outcomes: A panel study using the legalized population survey. _Industrial Relations: A Journal of Economy and Society 50_ (3), 443–471.

- Amuedo-Dorantes, C., C. Bansak, and S. Raphael (2007). Gender differences in the labor market: Impact of irca’s
in these two countries. In the Appendix we show that the rank correlation coefficient is high for the analytical and non-routine manual skills measures (0.75 and 0.67), is moderate for the interpersonal and routine manual skills measures (0.57 and 0.49), and negative for the routine cognitive measure (-0.77).

# **9. Summary and Discussion of Results**

In this paper we have analyzed the simultaneous impacts and interplay between a trade measure (value of goods exports per worker at the industry level) and a technology measure (share of households with a computer at the province level) on the demand for different types of skills and aggregate labor market indicators in Indonesia over the period 2005-2015. We based this analysis on four sources of data –the Indonesian National Labor Force Survey, the Indonesian National Socio-Economic Survey, the STEP Survey for Philippines, and the UN Comtrade database.

The analysis was separated into two sub-periods marked by a change in the focus of exports. The sub-period 2005-2010 was characterized by a commodity boom, while 2011-2015 exhibited a reduction in the value of exports that was larger for the mining industry than for manufacturing industries. Our conjecture is that this change implied an important shift in the demand for different types of skills as minerals, the main commodity of the country, used non-routine analytical and interpersonal skills more intensively in comparison to other industries, while manufactured products were more intensive in the use of non-routine manual and routine skills.

We hypothesized that our measure of the technological development available to a firm, the share of households with a computer in a firm’s province, is related to the structure of employment by skills in that province. However, the direction of the effect is not clear because the technology measure may be signaling the use of different types of technologies, not just computers, and these different technologies may have differing effects on the demand for skills. On the other hand, we expected firms facing an increase in the foreign
The analysis also examines the impact of tropical cyclones on fatalities. The analysis finds that almost all deaths from tropical cyclones occur in just two countries: Myanmar and especially Bangladesh. The impact of climate change on tropical cyclone deaths depends on what happens in these two nations. Since the model predicts that tropical cyclones in the Indian Ocean might get more intense but more likely will get less intense, deaths in these two countries are expected to fall. Consequently, global deaths are expected to fall.

There are many uncertainties associated with the forecasts made in this study. The emission path of greenhouse gases is highly uncertain because it depends upon the long term growth of the economy, the long term relationship between GDP and energy, and any government mitigation policies that may be adopted over the next century. The relationship between climate change and greenhouse gas concentrations is also quite uncertain as revealed by the different projections by the four climate models used in this analysis. Exactly how tropical cyclones will react to climate change is also uncertain as it depends upon many factors that are difficult to predict. Finally, the magnitude of the damages that future tropical storms will cause is uncertain. The damages with respect to storm intensity appear to be very sensitive to minimum pressure. The damages are also very dependent on the elasticity of population and especially income. However, the vulnerability parameters are quite different depending upon whether one uses US or international data. There is a clear need for better international data that includes the intensity of storms as well as their location. How damages might change if there is both a change in tropical cyclones and sea level rise has barely been analyzed. Nicholls et al. 2008 suggest an additive effect between sea level damages and storm damages. But the interaction between these two effects may be more than additive. This is clearly a topic that deserves further research.

Finally, how society will adapt to tropical cyclones in the future is not yet clear. Currently, many countries have mal-adaptation policies that make matters worse by encouraging assets to remain
Table A10: Medium-term impacts of _Mais Educação_ on educational outcomes for schools that chose environmental education field

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|2.346|–5.090|–1.626|
||(2.009)|(3.967)|(3.373)|
|_R_ <sup>2</sup>|0.755|0.811|0.823|
|Observations|296|296|296|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|3.569|–5.655|–6.054|
||(2.650)|(4.563)|(4.450)|
|_R_ <sup>2</sup>|0.801|0.793|0.764|
|Observations|236|236|236|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined environmental education field in 2008 and remained in the activity until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is Portuguese grades in _Prova Brasil_ , in column (2) mathematics grades in _Prova Brasil_ , and in column (
Policy Research Working Paper 5535

# **Abstract**

This paper draws lessons from an original randomized experiment in Malawi. In order to understand why roads in relatively good condition in rural areas may not be used by buses, a minibus service was subsidized over a six-month period over a distance of 20 kilometers to serve five villages. Using randomly allocated prices for use of the bus, this experiment demonstrates that at very low prices, bus usage is high. Bus usage decreases rapidly with increased prices. However, based on the results on take-up and minibus provider surveys, the experiment demonstrates that at any price, low (with high usage) or high (with low usage), a bus service provider never breaks even on this road. This can contribute to explain why walking or cycling is so widespread on most rural roads in Sub-Saharan Africa. In terms of policy implications, this experiment explains that motorized services need to be subsidized; otherwise a road in good condition will most probably not lead to provision of service at an affordable price for the local population.

This paper is a product of the Transport Department, Africa Region. 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://econ.worldbank.org. The author may be contacted at graballand@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
, the median scaled employment IRF for each country is treated as the dependent variable in cross-section regressions containing potential structural factors associated with the employment effect.

Proxies for the degree to which economies experiencing large scale technological change are rare, particularly on a consistent basis across countries. Sectoral employment shares, both in levels and how much they have changed in recent decades are one proxy for economies going under significant technological transformation. In particular, the literature has widely found that in advanced economies, manufacturing jobs have particularly been at risk of replacement, making industrial employment, which includes manufacturing and is widely available across economies, a useful covariate to test. Data on sectoral employment shares are taken from the World Bank’s World Development Indicators, as are data on foreign direct investment net inflows, which may also be a proxy for the implementation of new technologies. Educational attainment is measured 13
as during bathing, and the corresponding time stamps registered by the device as non-wear were marked and discarded accordingly. One additional consideration was that approximately 10 percent of the entries had reported secondary activities simultaneous with the primary activities. From a modeling standpoint, these dual activities introduce more uncertainty instead of clarity. Therefore, such entries were removed from the analysis.

Lastly, we scrutinized the minute level data to identify extreme outliers within 15-minute level epochs. The data-points deviating more than two-sigma from the sample distribution were discarded. This means that all data points diverging from the general behavior pattern of a specific activity within the 15-minute epoch were excluded from the dataset. The discarded outliers accounted for 2.6 percent of the data. The remaining data were considered suitable for the scope of the current study. The cleaned dataset comprised 388 unique participants, indicating, that 27 participants were removed during the cleaning process from the original sample size of 415 participants.

# **3.3 Feature engineering**

The raw data comprises four base variables: accelerations along three axes and the step count. Additional features were engineered from the extracted base variables of the triaxial accelerometer. In the realm of data science, a feature is a characteristic of data. These are used to enhance data analysis. In addition to the base and generated features, we incorporated extra features based on background information and on the timestamp of the time-use activities, representing the day of the week (weekday or weekend day) and segment of the day (am or pm).

We conducted a correlation analysis on the feature engineered data to avoid unnecessary growth of the data space. Correlated variables can add more weight to a variable without adding more value to a model. All highly correlated variables were identified and excluded from the dataset. A Pearson’s correlation (or anticorrelation) with a threshold _r_ -value of 0.95 was adopted.

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examples of delinking, until the stock market crash of October 1987, the profitability of specialized financial institutions, such as investment banks, outpaced that of industry and commerce, and the cumulative gains in share prices exceeded those of any other financial or economic indicator. For instance, between 1982 and 1986 the cumulative real GDP growth in OECD countries was 13 percent. By comparison the world index of financial market growth increased by 130 percent over the same period.44/ Although delinking and asymmetry in the rates of growth of financial services and of other sectors of the economy are not bad per se, they are a source of tensions. For example, the fact that financial decisions are often divorced from underlying dievelopments in the real economy raises the level of economic uncertainty. The stockmarket crash of 1987 shows that such risks cannot be underestimated: on October 19 the band-wagon effect of simultaneous action by major institutional investors and feedback between the cash and futures markets precipitated the market collapse and rekindled fears of an imminent global recession.

Admittedly, the continued momentum of growth in industriaL countries, and the limited impact of negative wealth effects--following the fall of share prices--have shown that those recessionary 5ears were largely exagerated at the time.45/ However, the point remains valid that there is a need to 44/ Bernard Snoy: Le decouplage entre la finance et l'economie et la crise financiere d'Octobre 1987; Processed, IEC, Brussels. While this disparity in price performances is not unique--it is a common feature of past "bull markets"--it had become more pronounced then in the past.

45/ The IMF has substantially revised its of real GNP growth in industrial countries. In October 1987, these were estimated at 2.4% for 1987, and 2.6% for 1988. (World Economic Outlook, IMF, October 1987, Table A2, p. 40.) In April 1988, these estimates were revised to respectively 3.1
I extend the model to allow banks to choose the level of distortion, where a higher level of distortion is associated with a lower cost. When financial integration is associated with a large drop in the price of funding, banks endogenously select a higher level of distortion. Intuitively, when funding is cheap, most projects should be implemented anyway; the returns to sustaining institutions that enable the differential contraction of productive and unproductive projects goes down. Interestingly, these endogenous institutional changes have a mixed effect on output and output volatility. While the output response to large contractions in the supply of funds is amplified, the deterioration in the financial system actually serves to increase and stabilize output during normal times. In the extreme case, the financial system implements all projects, regardless of small fluctuations in the price of funding, resulting in high and stable output. However, when the supply of funds contracts so much that the economy can no longer afford to run at full capacity, there is an efficiency loss resulting from the economy’s inability to absorb contractions through the discontinuation of the least productive projects.

The model’s predictions depend crucially on the assumption that the marginal return to funding is declining less steeply in more distorted economies. Formally, the key assumption is that the marginal return to funding is log supermodular in funding and the level of distortion. To assess the relevance of this assumption, one can ask (i) under which microfoundations it will hold and (ii) whether or not these microfoundations are themselves empirically relevant. I show that this assumption arises naturally in a wide variety of models in which the equilibrium is characterized by a stochastic matching between funding priorities and projects. Distortions create a mismatch, making better projects less likely to receive high priority, and relatively worse projects relatively more likely to receive a higher priority. I present empirical evidence for these properties using firm level data from emerging and developed economies. I find that in emerging economies, productive firms are relatively more sensitive to fluctuations in the aggregate supply of funds
Conditional on reporting social security data, offshores are much larger both in average number of employees (43 versus 13 for onshores) and wagebill (380,000 dinars versus 180,000 dinars for Onshores). They are also much more likely to report export revenue in their tax returns (60% versus 12% for onshore) and report average export revenue an order of magnitude larger. Similarly, they are also more likely to have export values in customs data and also larger values on average.

Despite representing a relatively small share of total firms, a disproportionate amount of economic activity in Tunisia is concentrated in offshore firms, particularly for the manufacturing sector. In Figure 2, we document the importance of the offshore sector across the period, both for the entire sector and specifically for manufacturing. In panel (a), we show that offshore firms were approximately 20% of all firms in the period, but represented a larger share (25-30%) of all manufacturing firms - although that share declined constantly over the period. Since we already documented that Offshore firms are on average larger than onshore ones, it is not surprising that they represent a larger share of employment (40%) and revenue (20 - 25%) - for manufacturing, they are substantially more important, representing close to 60% of total employment and 35-40% of aggregate revenue. Close to 80 - 90% of all exported value is accounted for by the offshore sector, which is consistent with exporting firms concentrating in the offshore regime given the substantial benefits that they derive.

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# **Conclusions**

This work presents a mapping approach specifically designed for the agricultural systems of northern Nigeria. This approach has the potential to be replicable in successive years and expanded to other locations within the Sudan-Sahel ecozone. The present work underscores the relevance of incorporating expert knowledge in the design-mapping strategies in regions where other data sources are scarce. It also highlights the importance of interannual land surface dynamics to achieve a robust characterization of land use in the study area. Likewise, it demonstrates the potential of dense stacks of medium-resolution imagery to capture these dynamics.

The mapping framework was applied to produce a 30-meter spatial resolution LCLU map focusing on the agricultural landscapes of northern Nigeria for 2015. The map provides up-to-date information at higher spatial resolution and better characterizes agriculture in a region with limited land-use information, where agriculture is the main livelihood strategy. The map shows a high farming intensity and a cropland area that is significantly higher than in the rest of the country. Rain-fed agriculture systems already occupy most of the landscapes—at the expense of natural vegetation—and the majority of floodplains suitable for irrigation agriculture are already under use. The map also identifies the landscape variations associated with the region’s welldefined north-south gradients.

In a region under increasing environmental and demographic stress, this work highlights the potential of multitemporal medium-resolution satellite data to generate detailed, up-to-date landuse information. This type of information is essential for designing efficient policy interventions, improving famine-related early warning and response systems, and understanding the links between land use and agricultural development, migration, and conflict.
Bollen at al. (2001) examined the performance of several alternative proxies for economic status. They conclude that if researchers’ focus is on economic status itself (as is the case when using proxies to identify the poor), then the choice of proxy can make a difference. If, however, attention lies on other variables with economic status being used as a control, then the noneconomic status variables are relatively robust to the choice of proxy.

This study has the unique opportunity to evaluate the ranking at a regional level, and administrative level using both an asset index derived using census data and imputed consumption. Using the urban and rural dummies used in the asset index, it is possible to construct separate maps for the proportion of household in the poorest 40 per cent quintile as ranked by the asset index (Figures 8 and 9). Those maps are directly comparable with the headcount FGT(0) using the Poverty line as the 40 per cent of lowest quintile (215,235.2 Manat per month) (Figures 1 and 2). From a simple comparison of the maps it is clear that that the two approaches produce significantly different rankings at the rayon level.

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