rafmacalaba/data-use-mentions-extended huggingface dataset:
A corollary of the negative relationship between SPEI and conflict incidence would be that investments in infrastructure and technologies that reduce exposure to such climate-induced variability in the soil water, such as irrigation infrastructure, may reduce the risks of conflict. However, this section adds a nuance to this argument, presenting evidence documenting that such investments may make land a more valuable target and thus result in increased conflict incidence during times of heightened instability, especially in high-scarcity settings such as the Sahel.

This section exploits an exogenous increase in conflict in Western Africa in the post-2011 period, sparked in by events coinciding with the Arab Spring.<sup>7</sup> Trends in battle events from ACLED data in Western Africa over the period 1998 – 2017 show a sharp uptick in conflict events in Western Africa from 2011 onwards, in the aftermath of the Arab Spring (Figure 3, Panel A). It has been suggested that the Arab Spring, and in particular the fall of the Ghaddafi regime which supported various political and business interests in the Sahel region and other African countries, created a shift in the region’s economic and political equilibrium _Figure 3: Conflict in Western Africa (1998 – 2017)_ <!-- Start of picture text --> Panel A<br>Conflict in West Africa<br>2000 2005 2010 2015 2020<br>year<br>.1<br>.08<br>.06<br>Share of Cells with Battles<br>.04<br>.02<br><!-- End of picture text --> <!-- Start of picture text --> Panel C<br><!-- End of picture text --> <!-- Start of picture text --> Panel B Panel C<br>Rest of West Africa G5 Sahel<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>year year<br>Irrigation Exists No Irrigation Irrigation Exists
# 1. introduction

As did the rest of the developing world, low-income African countries borrowed heavily (though mainly from noncommercial sources) in the period after 1975 to finance consumption and domestlc capital formation in the face of adverse terms of trade shocks and weak growth in export demand from the developed countries. This borrow-ag not only helped smooth out absorption in the face of temporary income shortfalls, but also allowed aL number of countries to maintain macroeconomic and sectoral policies that by the early 1980s appeared unsustainable in the long run (World Bank, 1981). When financial inflows dried up in the early 1980., in the middle of a sustained slump in commodities prices and at the onset of a drought that affected large parts of the region, many countries were forced to face the reality that existing levels of absorption were no longer sustainable even in the short run. The adjustment to this reality has been harsh. Table 1 summarizes the extremely poor macroeconomic performance of sub-Saharan Africa in the 19809, both in absolute terms and relative to the rest of the developing wo%ld.

This paper reviews the literature and country experience on the macroeconomics of fiscal policy in low-income Africa.<sup>3</sup> Given the experience of the 19708 and 80s, the ultimate focus of the discussion must be the interaction of fiscal policy with the challenges of stabilization and structural adjustment. Two dominant themes emerge for fiscal policy. The first is the importance of overall fiscal restraint as a precondition for 3 The paper draws on unpublished World Bank material on the following countries% Botswana, Cote d'Ivoire, Ghana, Kenya, Sierra Leone, Tanzania, Zaire, and Zambia. With the exception of Botswana and Cote d'Ivoire, these countries are all in the World Bank's category of 'Low-income, (per capita income below $400 in 1985). Botswana and Cote d'lvoire, with per capita incomes of $840 and $660. respectively, are classified as ZMiddle-income.

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## **D Do Treated Businesses Improve Performance at the Expense of the Control Group?**

A final concern relates to how the gains in sales and profits are achieved. Specifically, are these gains achieved at the expense of the control group? In order to allay this concern, we present two key pieces of evidence. First, a simple comparison of sales and profits for the control group between baseline and endline shows that trends in both these variables are flat. Table 1 shows baseline sales and profits of USD PPP 4,454 and USD PPP 890, respectively. In comparison, Tables 6 and 7 show control group average profits and sales across the endline period of USD PPP 4,999 and USD PPP 895, respectively. Neither of these performance variables are statistically different from baseline to endline, confirming a flat rather than declining growth trajectory.

Second, we use GPS data to directly measure linear distance from each shop in the control group to its nearest shop in the treatment group. If treated shop owners were achieving gains in sales and profits at the expense of the control group, then shops closer by would arguably suffer bigger losses in performance than shops further away. Appendix Table A9 presents a simple regression of sales and profits on this distance measure, with all standard baseline regression controls. The coefficients on both variables are in fact negative and not even close to statistically significant – standard errors are larger than the coefficients for both variables.

Both these pieces of evidence suggest that the gains in performance were, in fact, not achieved at the expense of the control group.<sup>25</sup>

# **VI Conclusion**

This paper shows that it is possible to stimulate efficiency and growth among small firms by sharing information on the best practices of successful local business peers. The analysis finds that the delivery channel of this information is critical – pure information alone does not have an impact on the adoption of business practices or on performance, but combining it with simple and inexpensive
associated actors in each event. The recorded events are based on accounts from media, NGOs, international organizations, partner reports, and new media (e.g., social media such as Twitter or Facebook). A single account can include several events (as would be the case of one news article describing a series of violent attacks), however, the ACLED database includes one observation per event-location.

We use the detailed information provided by the ACLED project to code individual events. Since the ACLED project also provides information on events such as treaties and peaceful protests, events are classified as i) violent and/or ii) herder-related. Violent events are defined as those with an ACLED coding with a clearly defined violent event type or sub-type. These events include attacks, murders, and kidnappings, but also civil unrest (e.g., mob violence), which may emanate from reprisal or public reaction. The violent destruction of property (such as arson or burning of fields) is also included, given that such events were frequent around the farmer-herder conflict. All fatal events are considered violent.

We code all incidents as herder-related if they include the terms “pastoralist,” “herder,” or “herdsmen” as either an actor or associated actor.<sup>25</sup> As noted above, the incident data largely rely on public accounts, and so one risk is that the narrative framing of events could affect the terms used to record each event in the ACLED database. This would occur if terms like “herder” are used when these groups were considered perpetrators, but if another term is used when such groups were the victims of, say, reprisal violence. To

> 24Nigeria includes 6 geopolitical zones, which are one administrative level up from states.

> 25Or variants of these terms, including e.g. “Herder.”

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the participation of women in the labour force, and provides few policy-relevant conclusions with respect to the different economic, financial, and social needs of households of different demographic compositions. The study’s first contribution to the literature is on the definition of a household typology for Jordan, which is an exercise that varies across countries. In the absence of a standard typology, we define three groups as an empirical exploration. We construct two typologies based on households’ demographic characteristics, such as their composition by age, number of workers, children in school and pensioners. These characteristics play a significant role in determining which fiscal taxes and transfers a household experience and to what extent. They also influence the participation of women in economic activity outside of the household because they affect the generation and allocation of care responsibilities—how many and who bears them. Due to gendered social norms women often bear a larger burden, which in turn can prevent them from looking for paid employment, or restrict the type, duration, or location of work. We also define a third typology based on the participation of men and women in household income.

Once the household typologies are created, the next step is to assess how much different types of households in Jordan contribute to and benefit from current fiscal policies and the extent to which the fiscal system is helping to equalize post-market outcomes within and across groups. For this part of the analysis, we focus on the fiscal and care groups. Which types of households are benefiting or contributing more to the fiscal system? How much is the fiscal system in Jordan doing to reduce inequality across households? How much is it doing to reduce poverty for the households that need it the most? These are the questions that this study is set to answer.

> 2 Analyses are generally conducted at the household level as data is often only available at this level and the benefits and levies of fiscal policy may not apply at the individual level.

> 3 According to DoS figures from the 2018 LFS, 14 percent
especially over a longer period (Minoiu and Reddy, 2009). Case-study evidence seems more consistent with the _Assessing Aid_ argument. Empirical work based on cross-country data remains work in progress (Temple 2010; Arndt, Jones and Tarp 2011). 9  The weight of the portfolio implementation experience was increased at the time of IDA12 in recognition of its relevance to the aim of allocating IDA funds where they may be expected to be used most effectively.

> 10 The Country Performance Rating is raised to its 5th power and multiplied with population and GDP per capita raised to a negative power (-0.125) to direct more IDA resources to its poorer members out of equity considerations.

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a location, say France, cannot be analyzed in isolation from alternative destinations available, like Germany. As a consequence, changes in the relative attractiveness of Germany will also affect migration incentives and, therefore, flows to France.<sup>3</sup> Our dynamic model, however, exposes another channel: the option value of available destinations for future mobility decisions. The ease of movement between a pair of countries affects migration from third countries. For example, when Poland joins the European Union and gains visa-free access to the internal labor market in the United Kingdom, more Belarusian migrants might decide to move to Poland as they hope to eventually end up in the United Kingdom. Similarly, closing down bilateral corridors might not fully eliminate migration from certain origin countries. As long as large income gaps exist, people will try to find a path to a specific destination via transit countries, no matter how circuitous that route might be. In other words, the availability of transit routes might significantly reduce the effectiveness of restrictive migration policies.

Using the estimated parameters of our dynamic migration model, we perform several simulation exercises where we ‘close’ certain bilateral corridors to highlight the spillovers between alternative locations as discussed above. We chose the migration corridors based on their importance for immigration to the United States, where detailed transit migration data are available. The migration paths we consider are direct migration (i) from Canada to the United States, (ii) from developing countries to Canada, and (iii) from developing countries to the United States. Our fourth and final simulation blocks all transit migration to the United States and only allows direct migration from the migrants’ birth countries. In each of these scenarios, we analyze how these policy experiments would affect migration from different countries to Canada, to the United States, and to other transit countries, like the United Kingdom. As expected, there are significant spillovers. For example, when people cannot move to the United States directly (Simulation 3), both permanent
households which attrit to those who do not.

These results suggest that while attrition is more common amongst households which experience greater income mobility, the magnitude of the bias is likely to be rather small. However, a concern might be that households which experience the largest absolute changes in income move houses and attrit out of the survey before the next quarter’s survey can be completed. Since these income movements are by assumption unobserved, we can not directly examine them. Instead, in Table 4 we examine how much our pseudo-panel estimates of absolute and conditional mobility differ when we consider only households which don’t attrit. We classify households according to whether they participate in all five quarters of the ENEU survey or not, and restrict our analysis to the cohort-quarter observations where we have at least 100 observations per cohort in each group. Column 1 repeats the quarterly pseudo-panel estimate of β in the absolute mobility regression for the full sample. Column 2 creates a pseudo-panel of non-attritors by taking the first wave observations for households which are observed in all five waves. Column 3 creates a pseudo-panel of attritors, by taking the first wave observations for households which are not observed for at least one of the four remaining waves. The estimate of β for the non-attritors pseudo-panel of 0.987 is very close in magnitude and not statistically different from the 0.991 coefficient for the full sample. The attritors pseudo-panel estimate of 0.977 suggests slightly greater absolute mobility among the attriting households<sup>11</sup> , but one can not reject equality of the coefficients in the non-attritors and attritors samples. These results therefore suggest that there is very little bias from attrition in estimating mobility with a balanced panel.

Columns 4 through 8 examine conditional mobility of the non-attriting and

> 11 Note that households in this pseudo-panel are by definition households that would attrit in the next 4 quarters whenever you sample them. This is a subset of the group of households which happen to attrit in an observed five-quarter period. They are thus households which
policy relevance, which is that it could show how differences in camp location and administration affect refugees.

This paper aims to analyze how the living arrangement (camp vs. out-of-camp) affects Syrian refugees' QOL by studying the case of Syrian refugees hosted in Jordan. Besides its policy relevance, another reason for looking at Syrian refugees in Jordan is availability of data. The Syrian Refugee and Host Community Survey (SRHCS) implemented in 2015 in Jordan allows comparing living conditions of separate samples of out-of-camp and in-camp refugees, as well as a sample of a host population. While Jordan hosts Syrian refugees in two refugee camps, Zaatari and Al Azraq, most refugees live outside camps in urban, peri-urban, and rural areas of the country. How has the decision to live out-of-camp improved their QOL? Does living outof-camp reduce deprivations and vulnerability of female-headed refugee households? Moreover, does living in either of the two camps affect the refugees' QOL? This paper investigates these issues.

Data are analyzed using the difference-in-difference and propensity score matching methods. Combining these methods helps control selection bias and other unobserved variables that could bias estimates of causal effects. Multidimensional indicators are used to measure QOL to capture deprivations that cannot be measured by income indicators alone. These indicators include life satisfaction, satisfaction with access to services, household assets, risk of overcrowding, income and poverty levels.

The results show that living in a camp reduces QOL for refugees. On average, refugees in camps are 36 percent more likely to live below the national abject poverty line, meaning that they find it difficult to meet daily basic needs. They are 37 percent more likely to live in overcrowded shelters. They own fewer household assets than refugees living outside camps (-2.85 assets) and are less satisfied with water, electricity, and sewerage access. They also report lower life satisfaction by 0.76 step in a 10-step Cantril Ladder measurement of life satisfaction. The QOL indicators differ by gender. Female-headed refugee households living in camps were more deprived of material
- Buckley, R. & R. Madhusudhan (1984), “The Macroeconomics of Housing’s Role in the Economy: An International Analysis,” Presented to the American Real Estate and Urban Economics Association;

Buckley, R. (1996), “Housing Finance in Developing Countries,” McMillan, London.

- Calderon & Serven (2008), “The Effects of Infrastructure Development on Growth and Income Distribution”, The World Bank Policy Research Working Paper (WPS3400).

- Chen, J. & A. Zhu (2008), “The Relationship between Housing Investment and Economic Growth in China: A Panel Analysis Using Quarterly Provincial Data,” China National Social Science Foundation Working Paper.

- Christiansen, L., J. De Weerdt & Y. Tod (2013), “Urbanization and poverty reduction - The role of rural diversification and secondary towns,” Policy Research Working Paper 6422.

- Dübel, Hans-Joachim (2007), “Does housing finance promote economic and social development in emerging markets?,” Housing Finance Impact Study for International Finance Corporation.

- Econsult Corporation (2009), “Potential economic and fiscal impacts of a Pennsylvania housing trust fund,” The Housing Alliance of Pennsylvania.

- Egert, B. & D. Mihaljek (2007), “Determinants of house prices in Central and Eastern Europe,” _Comparative Economic Studies_ , vol. 49(3), p.p. 367-88.

- Erbas, S. & F. Nothaft (2002), “The role of affordable mortgages in improving living standards and stimulating growth: A survey of selected MENA countries,” IMF Working Paper.

- Fox & Sohnesen (2012), “Household enterprises in Sub-saharan Africa – Why they matter for growth, jobs and livelihoods.” World Bank Policy Research Working Paper;

- Freire, Mila, M. Gautier and O. Hassler (2006), “Review of Argentina’s housing sector: Options for affordable housing policy,” World Bank Working Paper
|Total|Intl|High-Tech|Total|Intl|High-Tech|
|U.S.|0.147***|0.057***|0.130***|0.472***|0.129***|0.428***|
||(0.010)|(0.010)|(0.011)|(0.021)|(0.028)|(0.017)|
|Euro+|0.102***|0.082***|0.068***|0.087*|0.121***|0.138***|
||(0.026)|(0.006)|(0.006)|(0.046)|(0.017)|(0.024)|
|Adv. Asia|0.143***|0.074***|0.123***|0.239***|0.111***|0.208***|
||(0.028)|(0.011)|(0.024)|(0.014)|(0.013)|(0.036)|
|other|0.110|0.054|0.092|0.402***|0.332***|0.346***|
||(0.127)|(0.130)|(0.135)|(0.044)|(0.031)|(0.059)|
|No. Obs.|5,317|5,317|5,317|5,317|5,317|5,317|
|R-Squared|0.687|0.652|0.706
Figure 9: Size of establishments by age cohort: Manufacturing <!-- Start of picture text --> Burkina Faso (2015) Cameroon (2008)<br>Ghana (2013) Rwanda (2013)<br>Mean employment size Median employment size<br>Entrant 1−5 years 6−10 years 10+ years Entrant 1−5 years 6−10 years 10+ years<br>Entrant 1−5 years 6−10 years 10+ years Entrant 1−5 years 6−10 years 10+ years<br>30<br>20<br>10<br>0<br>8<br>7<br>6 6<br>5<br>4<br>4<br>2<br>3<br>2 0<br>4<br>3.5<br>3<br>2.5<br>2<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the select countries; see section 3. _Note:_ The figure is constructed based on data for establishments in the manufacturing sector. Establishments with missing employment or age data and state-owned are excluded.

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.

**Tariffs on all services are zero.

Source: GTAP 9 database for tariffs; Jafari and Tarr (2015) for FDI barriers; cross-border services non-tariff barriers are mapped into our sectors from estimates by Francois, Hoekman and Woerz (2007); time in trade calculations are trade-weighted from data in Minor (2013); and AVEs in goods from Kee _et al._ (2009).

50
. Assess. Earth Obs._ , page 91–118.

- Elvidge, C. D., Baugh, K., Zhizhin, M., and Chi Hsu, F. (2013). Why VIIRS data are superior to DMSP for mapping nighttime lights. In _Proceedings of the Asia-Pacific Advanced Network_ .

- Ethiopian Roads Authority (2015a). Road sector development program 19 years performance assessment.

- Ethiopian Roads Authority (2015b). The road sector development program phase V.

- European Space Agency (2017). 300 m annual global land cover time series from 1992 to 2015.

- European Space Agency (2019). New Release of the C3S Global Land Cover products for 2016, 2017 and 2018 consistent with the CCI 1992 – 2015 map series.

- Faber, B. (2014). Trade Integration, Market Size, and Industrialization: Evidence from China’s National Trunk Highway System. _The Review of Economic Studies_ , 81(3):1046–1070.

- Fiorini, M. and Sanfilippo, M. (2019). Roads and jobs in Ethiopia. WIDER Working Paper 2019/116.

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by IEG‘s 2011 Annual Review, while a rather high share of public sector operations is rated as successful, overall progress on public sector reform was only observed in 47 percent of completed and reviewed country programs (IEG 2011: 8).

A third factor is the fact that a good share of public sector operations has been focused on more propitious environments – while at the same time this has been combined with a better than expected performance in more challenging environments. To a degree, the expected pattern of project performance - more likely to be good in better and worse in worse environments - _broadly_ holds also for Bank operations, but the relationship is less strong than might be expected. This is reflected in graphs 2 and 3 (covering all public sector operations and IL operations specifically).<sup>28</sup> Operations that are rated as moderately unsatisfactory or worse are more likely to have been implemented in a more difficult environment than operations that were implemented in more propitious contexts; and there have been no unsatisfactory operations in a propitious environment. As would be expected, the association between the environment and operational outcome ratings is somewhat stronger for ILs and weaker for DPLs.

> 28 Note that four of the countries appear in more than one category, i.e. they had multiple operations with different outcome ratings. The chart reflects the governance indicators for  ̳government effectiveness‘ from the World Governance Indicators (WGIs) compiled by the World Bank Institute (WBI) for 2000 and for 2010. While methodological shortcomings of the WGIs have been widely discussed, they offer a useful rough gauge of country context.

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