rafmacalaba/data-use-mentions-extended huggingface dataset:
17 Urban texture variables indicate that LAC cities have a quite dense texture, with values of intersection density (Figure 5e) and street density (Figure 5f) that are rather high. The _Circuity_avg_ values (Figure 5d) indicate that the street network layout in LAC cities is very regular: they are very close to 1, and 75% are below 1.05, meaning that most of their street networks are about only 5% longer than if they were all composed of straight lines. This regularity is in line with the findings by Huang et al. (2007). However, a general small increasing trend is observed in _Circuity_avg_ values from 1996 to 2010, which might be due to recent occupation of rugged terrain in mountainous areas.

The land use variable, sprawl (Figure 5g), measures how uniform is the spatial distribution of population within the urban extents. In general, this variable shows medium values with a small decrease from 1996 to 2010. This could be a weak trend towards a more uniform distribution of the population within urban extents.

The diversity in climate and topographic conditions across the LAC region translates into a high diversity in the shape that urban areas have taken. We look at the 2010 data to highlight country level differences in urban areas (Figure 6). _nExchange_ boxplots from Figure 6 show that Venezuelan cities tend to be more elongated than most of the other cities in the region, while Bolivian cities tend to be more rounded. Bolivian cities tend to have more smoothed perimeters (high values of _nPerimeter_ ), and the cities with less smoothed perimeters are in México, Brazil, Argentina and the República Bolivariana de Venezuela. _Fullness_ boxplots show that lower values occur more frequently in Colombia, Ecuador and Peru than in other countries of the region, and the highest values are in Bolivia and Guatemala. Urban texture plots show that more regular networks are often denser than the organic ones: in general, the República Bolivariana de Venezuela and Guatemala have the cities with more organic street networks in the
omplex survey design<br>f the error terms and<br>dized variables. Mode<br>transformed to norma<br>data 1000 simulations|. All estimates a<br>Method 2 uses th<br>l 3.2 employs<br>lity using the B<br>are imlemente|17<br>(1<br>re obtained with<br>e empirical distr<br>variables in TA<br>ox-Cox method<br>d The true ove|.7<br>.9)<br>the population we<br>ibution of the err<br>2 standardized b<br>before standardiz<br>rt rate for TA 1|ights. Method<br>or terms. Both<br>y those in NP<br>ation. Imputed<br>is the estimate|1 uses the normal<br>methods employ<br>S 2020/21. Food<br>poverty rates for<br>directl obtained|

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Model 3.1 employs non-standardized variables. Model 3.2 employs variables in TA 2 standardized by those in NPS 2020/21. Food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. Imputed poverty rates for TA 2 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within
change in food, timber and biofuels industries, this is one of four core uncertainties a↵ecting competition for global land use (Steinbuks and Hertel, 2013). To quantify the uncertainty in agricultural yields, we construct stochastic crop productivity index that captures two key uncertainty sources: technological progress and global climate change (Lobell et al., 2009; Licker et al., 2010; Foley et al., 2011).<sup>5</sup> Following Rosenzweig et al. (2014), we use projections from climate and crop simulation models, as well as the survey of recent agro-economic and biophysical studies to calibrate the index.

We simulate the results of the perfect foresight model under di↵erent realizations of the crop productivity index, focusing our attention on the current century. We then compare and contrast them with the results of the dynamic stochastic model, where the uncertainty in crop yields is brought to the model’s optimization stage. When the uncertainty in crop productivity is incorporated into the model, we see an additional redistribution of land resources aimed at o↵setting the impact of potentially lower yields. Owing to intertemporal substitution, some of that redistribution takes place even in the absence of the actual changes in the states of climate or technology a↵ecting crop yields. Moreover, the range of these alternative optimal paths of cropland is considerably smaller than the magnitude of possible land conversion resulting from the scenario analysis based on deterministic model simulations. This result indicates that the scenario analysis may significantly overstate the magnitude of expected agricultural land conversion under uncertain crop yields.

Besides the methodological innovation, our study also contributes to the growing environmental economics literature that analyzes the intertemporal allocation of land and other natural resources under uncertainty and irreversibility constraints. Most of that literature focuses on a particular type of resource or sector, where intertemporal issues are significant and cannot be ignored. One example of this literature is forestry management in the context of uncertain fire risks and climate mitigation policies (Sohngen and Mendelsohn, 2003,
, straw (46), miscellaneous edible preparations (chapter 21) and wadding yarns and twine (chapter 56). While these numbers may indicate export potential for Syria in these sectors, it is important to note that the analysis relies on proximity indicators between products which are computed at the global level. This, in turn, implies that there may be impediments specific to the Syrian economy, which may render their production and export unprofitable.

# **3 Potential Determinants of Sectoral Export Diversification in Syria**

In the previous section, we analyzed the patterns of export diversification in Syria after the initiation of the reform agenda with the 9th Five-Year Plan in 2000. We found that 22
# **6.3 Impacts on Firm Performance**

Table 4 shows that we generally do not observe significant treatment impacts on business performance outcomes; although due to power limitations, we cannot rule out some sizeable impacts.<sup>12</sup> Treated firms report insignificantly higher unconditional profits than control firms in the last year (Column 1). A large part of this difference is driven by the larger number of control-group entrepreneurs who closed their original firm, did not open a new firm, and hence report zero profits, and so differences in conditional profits are smaller and also insignificant (Column 2).<sup>13</sup> Almost all firms who stay open report positive profits in the last year; thus there is no evidence of access to psychometric-appraised loans keeping “zombie firms” alive during the challenging macroeconomic circumstances. That said, treatmentgroup firms report significantly _lower_ profits in the last 30 days prior to the endline survey in October 2021 compared to control group firms (Column 4 randomization inference p-value _<_ 0.1; and Columns 3 and 4 of Appendix Table A3 show significant reductions in levels).

It therefore appears that access to psychometric-appraised loans kept more, and slightly smaller, firms alive; that this at least did not lead to weaker average performance among the treatment group in the last year; but that treatment-group firms were performing worse specifically around the time of the endline survey. It may be that firms in the treatment group are in specific product or service lines that were particularly exposed to the challenging macroeconomic climate around the time of the endline survey — which took place just after national elections, and prior to those a late peak of COVID-19 and restrictions. We do not see evidence, however, that the new firms that opened up in the control group are those with the highest profits in the last 30 days. Nor, as mentioned above, do we see that the new firms are distributed differently across sectors. Thus if the control group had greater
# **2.2 Humanitarian Shelter Program**

In Jordan, there is limited secure and affordable housing available for Syrian refugees. The UNHCR Quarterly Analysis of Refugees in Jordan found that in 2022, 25% of Syrian households perceived a current threat of eviction.<sup>4</sup> Additionally, one in four refugees applying for rental assistance from a major Jordanian humanitarian organization had moved at least three times in the previous year (NRC 2015), demonstrating the widespread precariousness of their housing situation. Refugees often rely on humanitarian assistance to manage rising rental costs, but these funds are insufficient to cover the full expense, and hence 70% of Syrian households living in local communities reported having rental debt (Hagen-Zanker et al. 2018; Lehmann and Masterson 2014). Frequent relocation due to rental affordability issues and a lack of stable rental contracts may also make it challenging for refugees to maintain necessary documentation and access public services (NRC 2015).

Given this situation, the implementing partner designed the Shelter Program to secure more stable housing for Syrian refugees living in host communities. The intent was also to use the program to facilitate social cohesion between refugees and hosts by including direct benefits for the landlords renting to refugees. It focuses on Syrian refugees in the country’s northern governorates of Irbid and Mafraq, which are adjacent to the Syrian border and contain a large share of the country’s displaced persons.

The overall Shelter Program is composed of several different assistance modalities, including projects focused on water and sanitation, eviction protection, and “inclusion kits” for individuals with disabilities. This study evaluates the impacts of a housing shelter modality, which we will refer to in what follows as the Housing Subsidy Program (HSP).<sup>5</sup> Under HSP, the implementing partner directly compensated Jordanian landlords for rent on behalf of their refugee tenants, with the refugee household receiving rent-free housing for 9 to

> 4Determined using the Vulnerability Assessment Framework, a bi-annual survey of registered refugees
# **References**

- Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018, January). Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. _Scientific Data_ , _5_ . doi: https://doi.org/10.1038/sdata.2017 .191

- Acheampong, E., Ozor, N., & Sekyi-Annan, E. (2014, 06). Development of small dams and their impact on livelihoods: Cases from northern ghana. _African journal of agricultural research_ , _9_ , 1867-1877. doi: 10.5897/AJAR2014.8610

- Adeoti, A. I., Barry, B., Namara, R. E., Kamara, A. B., & Titiati, A. (2007). _Treadle pump irrigation and poverty in Ghana_ (IWMI Research Reports No. 44520). International Water Management Institute. Retrieved from https://ideas.repec.org/p/ags/ iwmirr/44520.html doi: 10.22004/ag.econ.44520

- Altchenko, Y., & Villholth, K. G. (2015). Mapping irrigation potential from renewable groundwater in africa – a quantitative hydrological approach. _Hydrology and Earth System Sciences_ , _19_ (2), 1055–1067. Retrieved from https://hess.copernicus .org/articles/19/1055/2015/ doi: 10.5194/hess-19-1055-2015

- Aron, D. V., Aguilar, R. A. C., Diaz-Bonilla, C., Betran, M. G. F., Foster, E. M., Fujs, T. H., . . . others (2023). Poverty and inequality platform (pip).

- Aseyehegn, K., Yirga, C., & Rajan, S. (2012, 01).
remaining 2 percent were from other types of violence. However, despite there being significantly more fatalities from ground battles, the most common type of violence in the conflict was remote violence. Approximately two-thirds of violent incidents were instances of remote violence, 11We are only able to identify these groupings when using the ACLED data from 2016 and on. Although the Uppsala Data Program has some information on types of violence, they are not very compatible with the ACLED groupings. Furthermore, the vast majority of violence that occurred during our period of analysis occurs after 2015.

> 12Other types of violence are mostly violence attributable to terrorism or violence against civilians. 13As mentioned in the introduction, 34 percent of these non-displaced households were only interviewed once. And of the ones that were interviewed more than once, the largest share were interviewed only twice (15 percent of the total non-displaced sample), and all households that answered more than one survey tended to respond to surveys that were close in time and the choice of the exact survey is not important to the results. Importantly, all results are robust to using the first survey to which non-displaced households respond.

10
<br>**List of Figures**|. . . . . . . . . .<br>63|
|A.1 Electoral Data Checks: Turnout . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>41|
|A.2 Electoral Data Checks: Digit Distribution in Vote Tallies . . . . . . . . . .|. . . . . . . . . .<br>42|
|B.1<br>Composition of Village Governments<br>. . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>63|
|B.2<br>Sampling of Citizens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>63|

## **List of Tables**

|A.1 Balance Checks on Village Characteristics and Electoral Data . . . . . . . . . . . . . . .|. .<br>43|
|---|---|
|A.2 Balance Checks on Village Characteristics: Administrative Data . . . . . . . . . . . . .|. .<br>44|
|A.3 Balance Checks on Sampling of Citizens . . . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>44|
|A.4 Balance Checks on Citizen Demographics . . . . . . . . . . . . . . . . . . . . . . . .
taxes_

**Other important elements of the fiscal system can indirectly affect children.** These include indirect subsidies related to electricity and gas consumption, direct taxes comprising personal income taxes and social security contributions, and indirect taxes such as VAT and excises, covering fuel, alcoholic beverages, and tobacco.

**Our fiscal incidence analysis includes most of these fiscal interventions, building upon previous work by Vaughn and Cabrera (2020) but with a more detailed examination of benefits for families and children.** In this analysis, we included different interventions that allow for a fiscal incidence analysis, such as those included in the work of Vaughn and Cabrera (2020). A more detailed analysis of benefits for families and children is incorporated. For this category of social protection, the modeling detail was the one permitted by the SILC survey information and available administrative figures. Some programs appear "grouped," meaning they could not be identified individually. On the other hand, although some programs directly benefit children, since the CEQ methodology does not have an approach to measure the effect of in-kind transfers, they were not considered in the monetary poverty analysis. They are included in the inequality analysis. The interventions included in the CEQ and child poverty analysis are summarized in Table 1, Panel A. Moreover, we identify child-relevant budgets, that is, public spending or tax revenues that explicitly target child well-being (Cuesta et al., 2021), as well as those that indirectly affect them (table 1 panel b).

> 13 See Annex 1 for detailed parameters.

10
Table 7 presents estimates from specification (3). The coefficients of _Firstborn girl_ imply that FG families have more children and practice greater sex-selection at second and higher parities even if expected dowry is zero. The interaction coefficients are, however, highly insignificant for the fertility regressions implying that there is no significant differential effect of dowry by firstborn sex on future childbearing. For sex-selection, the interaction terms are also insignificant at conventional levels, but not as strongly as in the case of fertility. We obtain similar results from REDS data; see Table A.9.

These findings not only assuage concerns about endogenous fertility, but are also important in their own right. It is frequently claimed that dowry is an underlying cause of son preference, malebiased fertility, and discrimination against girls in India. Bhalotra et al. (2016), for instance, find that an unexpected one-time increase in the price of gold led to immediate rise in fetal and infant mortality of girls presumably because households perceived the gold price shock as an increase in the value of dowry. Similarly, Alfano (2015) finds that an amendment that made the Indian anti-dowry law stricter in 1985 led to decreases in male-biased fertility behaviors as it potentially made the dowry cost of daughters smaller. If dowry indeed causes male-biased behaviors, by the same reasoning, FG families should also be more likely to practice sex-selection at higher parities as expected dowry rises. However, we do not find strong evidence for this, with the caveat that our specification is quite different from those used in prior work. Our findings are consistent with Borker et al. (2017), who argue that dowry is not the root cause of sex-selection in India; instead, sexselection is driven by marriage-market frictions that arise from the way the institution of marriage is structured in India.

While the desire for at least one son is real, and affects childbearing decisions in India (Jayachandran (2017)), we find that it leads to higher fertility and higher sex ratios _even in the absence of dowry_
charge) are found. Tiered pricing for irrigation water is common in the state of California (Rao, 1988) and Israel (Yaron, 1997b). BolarLd and Wittington (2000) review the recent movement towards increasing block tariffs in developing countries.

# _2.2 Non-volumetric Methods_

In many cases, volumetric pricing is not feasible or desirable. In which case, non-volumetric pricing is utilized. There are several such pricing methods found in common practice for irrigation service: output pricing, input pricing, area pricing, and betterment levy pricing. Output pricing methods charge a water fee for each unit of output produced by the user. This requires knowledge of user outputs, but obviates the need for water use measurement (prohibitively expensive in many cases). In the case vwhere output is readily observable this method will save in transaction costs. Input pricing methcds charge users for water consumption through a tax on inputs. An example of this might be a pe r unit charge for each kilogram of fertilizer purchased.

Area pricing is the most common method of irrigation water pricing found. Bos and Walters (1990) in their survey of farmers on 12.2 million HA globally, found that in more than 60% of the cases water is charged on a per unit area basis. Under this pricing mechanism users are charged for water used per irrigated area, often depending on crop choice, extent of crop irrigated, irrigation method, and season. Rates are typically greater for pumped water from storage than for gravity flow from stream diversion. This method is easy to implement and administer and are best suited to continuous flow irrigation, which may explain its prevalence in the irrigated world (Easter and Welsch. 1986; Easter and Tsur, 1995). Area pricing does not necessarily imply lack of sophistication however. For exarnple the _warabandi_ system in Punjab, Pakistan and Haryana, India is a relatively complex system that combines elements of volumetric pricing with area pricing. This rotational method for equitable allocation of irrigation water fixes flows by day,
Policy Research Working Paper 10477

# **Abstract**

Using rich administrative data from the Netherlands, this paper studies the consequences of firm consolidation for workers. For workers at acquired firms, takeovers are associated with an 8.5 percent drop in employment at the consolidated firm and a 2.6 percent drop in total labor income. These effects are persistent even four years later.

The paper shows that the primary mechanism for this job loss is labor restructuring at consolidating firms. Workers with higher-than-expected pay relative to their human capital and workers with skills that are likely already present at acquirers are less likely to be retained.

This paper is a product of the Health, Nutrition and Population 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 dprinz@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
# **6 Conclusions and Recommendations for Researchers**

The use of remotely sensed EO weather data in economic contexts has become commonplace. Researchers use a variety of EO products to produce a host of different weather metrics to predict numerous different economic outcomes. If each EO product reported the same values for rainfall and temperature for a given location for a given time on a given day, then the choice of EO product would be immaterial. Unfortunately, this is not the case. Each product, while attempting to measure the objective truth of the weather, produces its own “truth.” The findings produced are not immutable truths.

Using LSMS-ISA data from six African countries in combination with nine EO weather data products, we demonstrate the lack of consistency in how EO weather products report on the weather. Importantly, the differences across products do not simply affect the cardinality, or relative size, of coefficients from different EO products. The shifts we see cannot be ascribed to an affine transformation of one EO product by another. Rather, what we document are changes in ordinality, the ranking of coefficients by size. In some countries one EO product may produce large, positive and significant coefficients while another EO product produces negative and significant coefficients. This order can swap if one moves to another country or if, within a give country, one changes the econometric specification or how the agricultural productivity is quantified. The unwary researcher may end up with results and draw conclusions that are a function of their choice of EO product and could have ended up with completely different results and conclusions if they had chosen a different EO product. Knowing this, the unscrupulous researcher can select the EO product that measures the “truth” in the way that produces the results and conclusion they want.

Given the lack of gold standard gauge station data in many countries, where does this disparity in how EO products measure the weather leave researchers who want to incorporate this type of data into economic contexts? As we cannot know the truth of what weather occurred