rafmacalaba/data-use-mentions-extended huggingface dataset:
availability tends to drive up food prices, cutting into their limited budgets and forcing painful trade-offs.

For our analysis, we combine the remote sensing satellite-based monthly weather data for 15 years (2006-2020) with the geo-coded 2019-2020 IE-LFS, a nationally representative cross-section of household data that collects detailed information on socioeconomic indicators and household consumption. Our sample comprises 14000 households, both rural and urban, from 1495 clusters<sup>3</sup> covering all 34 provinces of the country. We extract data on precipitation, Normalized Difference Vegetation Index (NDVI),<sup>4</sup> and land surface temperatures.

To identify the effects of the 2018 drought, we construct a standardized measure (zscore) of weather shocks for a cluster in the 2018 growing season based on long-run weather distribution in the country. The z-score for a weather indicator differences the 15-year national growing season average (over 2006-2020) from the cluster’s 2018 growing season realization and normalizes this difference by the country’s long-run growing season standard deviation. This allows us to identify anomalies in cluster weather realizations in 2018 from the _usual_ national weather distribution.

We exploit the spatial variation in the intensity of weather anomalies in the household cross-section to identify the effects of weather shocks. To allow for flexibility in the relationship between weather shock and consumption, we fit a partially linear model where weather anomalies enter non-linearly. To mitigate concerns around potential omitted variables, we include several household and cluster characteristics as controls that may explain consumption and district fixed effects that control for any district characteristics that may confound the effect of weather anomalies. However, there remain potential historical factors that may have affected welfare measures and weather, which we are unable to control for in our cross-sectional specification. In light of such omitted historical factors, our results can be interpreted as the contemporaneous impact of the 2018 drought on consumption and poverty in a conflict-affected state, rather than a measure of the longterm equilibrium effect of weather on welfare arising from such historical processes (Dell et al.
at the tariff line (Harmonized System 8 digits) are either zero or equal to the MFN tariffs.<sup>4</sup> We test the predictions of our model using data on preferential trade between the U.S. and Latin America. The empirical example investigated in this work is important since the U.S. is one of the wealthiest and largest economies in the world, and Latin America<sup>5</sup> constitutes a group of developing countries whose dependency on exports to the U.S. is signi fi cant.<sup>6</sup> Moreover, about half of Latin American exports enter the U.S. market through a myriad of preferential schemes.<sup>7</sup> The predictions of the model fi nd strong support in the data. Domestic importers and Latin American exporters’ lobbying activities signi fi cantly increase on average the U.S. tariff preferences granted to Latin American countries. Furthermore, the empirical exercise shows that U.S. importers (tend to) capture most of the rents generated by the tariff preferences granted to Latin American countries. These results offer a pessimistic view about using preferential trade schemes as a reliable source of gains for developing countries. These conclusions are robust to modi fi cations in the de fi nition of political organization, to the exclusion of agricultural goods, and to changes in the level of aggregation of the data.

The political economy of trade literature has been trying to evaluate the impact of foreign lobbying on U.S. trade policy. The relation between foreign lobbying and the determination of the U.S. MFN tariffs was investigated by Gawande, Krishna and Robbins (2004) using a Cournot-Nash competition model. They found that foreign lobbying decreases on average the MFN tariff applied by the U.S.. Kee, Olarreaga and Silva (2003) studied whether lobbying by Latin American exporters plays a signi fi cant role in the determination of the U.S. tariff
Emran, Greene, & Shilpi, 2018; Francesconi & Nicoletti, 2006). Nonetheless, Munoz and Siravegna (2021) show that the bias is small for estimates of absolute mobility that use census data. In addition, there is a low level of re-ranking when these estimates are used to rank economies across time and space by level of mobility relative to the ranking obtained with estimates that use retrospective information (i.e., surveys that ask all individuals for the level of education of their parents).

> 3I exclude several census samples for which this is not the case.

> 4More details can be found in the following link: https://usa.ipums.org/usa/chapter5/chapter5.shtml.

5
12 Table 1 provides on overview of the definition and data source of all conflict exposure proxies discussed in the following.

# **a) Child and sibling mortality**

A first proxy that captures replacement effects is whether or not a woman lost a child during the genocide (CHILDDEATH). The RDHS questionnaires record child mortality in great detail and even ask for the month of death. This allows us to precisely code CHILDDEATH to take the value one if a woman lost one or more children between April and July 1994. Moreover, we differentiate child death by gender in two further conflict proxies: SONDEATH indicates the death of at least one son during the genocide; DAUGHTERDEATH the death of at least one daughter. Fig. 2 displays the occurrence of child deaths over time as calculated from the 2000 and 2005 RDHS. Child deaths peak during the 1994 genocide, although child mortality remains relatively high in the immediate post-war period. Given that many mothers would have been killed in the genocide at the same time as their (young) children, this proxy somewhat underestimates the effects of genocide on child mortality. We interpret this proxy as capturing also the negative effects of conflict on health, sanitation and nutrition, leading to (even) higher child mortality.

A second proxy indicating replacement effects uses sibling mortality during the genocide (SIBLINGDEATH). Sibling mortality was recorded in the 2000 and 2005 surveys, but not in the 1992 RDHS. Every prime age woman was asked about all of her siblings born to the same mother. Of every sibling, information is available on the sex, date of birth, whether the sibling is still living, year of death, and whether the death was related to pregnancy or childbirth. This information is recorded irrespective of whether or not the sibling lives in the same household as the respondent and thus provides a good geographical coverage of deaths occurring across the whole country. However, no information is available on the sibling’s place of living or the place of death. The occurrence of sibling
as a result, the share of nonfood aid in total aid rises by about 11.3 percentage points (from 62.5 percent initially to 73.8 percent). The results are, of course, very intuitive: because the effect on public investment is larger (with an increase of about 7.9 percentage points of GDP in the long run), the effects on the growth rates of output and disposable income per capita, as well as on private investment, are magnified. As a result, poverty drops by a much larger amount than before—between 28 and 29 percentage points according to the survey data and with a partial elasticity of -1.8, and by 9 percentage points with an elasticity of -0.5.

Finally, instead of a permanent shock, one could ask if a “big push”, taking the form of a sizable, but temporary, increase in (nonfood) foreign aid, would not lead to similar outcomes. The results of this experiment are summarized in Table 7. They correspond to a temporary, 10 percentage point increase in the aid-GDP ratio over 5 years (from 2003 to 2008), allocated entirely to nonfood aid, which therefore increases by 17.3 percentage points in proportion of total aid. The results are, again, very intuitive: given the significant supply-side effects that the public capital stock exerts, a temporary but large increase in the flow of aid-financed public investment has a sizable effect on output and income growth. These effects persist for several years after the shock is reversed, because the supply-side effects of public investment are driven by stocks (which depreciate only slowly over time), not by flows. Although the growth rates of both real GDP and disposable income per capita turn negative after 2011, the reduction in the poverty rate, by 2015, is significant and similar in size to the results shown in Table 6.

Of course, an increase in aid of the magnitude displayed in Tables 5, 6 and 7 may
the main model that might confound the main effects. In column (1), we restrict the sample to non-north municipalities (distance _≥_ 350 km) and define a north municipality dummy that takes a value of one if its distance is below the median within the restricted sample. Column (2) in panel B defines the COVID dummy = 1 for the year 2019 instead of 2020. Column (3) controls for COVID-19 incidence rates at the municipal level measured as the number of cases per 100,000 inhabitants. Columns (4) and (5) include the value of per capita (pc) exports (in log) and the exposure to the CARES index at the municipal level as controls, respectively. _C_ -19 is a dummy indicator for 2020. All models include municipality and time-quarter fixed effects, and municipality trends, and are weighted by average *** ** * quarterly remittances at the municipal level. Standard errors are clustered at the municipal level. _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

21
. Two directions have been pursued. On the one hand, researchers have acknowledged that the conditions leading to the decision to flee create a selection process that in turn greatly influences the extent to which refugees and IDPs have been found to adapt to a new environment among their host communities. Where they settled is also not always random. On the other hand, while refugees, IDPs, migrants in general, and citizens all may live in the same location, they are often not subject to the same conditions

> 1The terms “forcibly displaced” and “forced migrants” are used interchangeably to denote refugees and internally displaced persons who have fled conflict, violence and persecution.

> 2Examples are the Cox’s Bazaar Panel Survey among Rohingya refugees in Bangladesh (World Bank, 2019a) and the Syrian Refugee Life Study (S-RLS) in Jordan (Miguel et al., 2022).

1
# **5 Conclusion**

“Is management good for the environment, or just hot air?”<sup>17</sup> Using firm-level data from 31 countries, our results suggest that management practices are associated with a reduction in energy expenditure intensity. However, these cost savings do not translate into reduced physical energy use, a close proxy for environmental impacts.

There may be several reasons for the divergence between the relationship of management practices to physical and expenditure measures of energy intensity. _First_ , managers may be focused on saving costs, not limiting the environmental impacts of energy use _per se_ . To the extent that management practices help managers reach their goals more expediently, it is perhaps not surprising that the correlation runs only through the energy expenditure intensity measure. _Second_ , management may enable firms to take advantage of lower prices, e.g. through investments in self-generation or by allocating production hours to take advantage of favorable time-of-day pricing. Larger one-time or repeat energy purchases may be subject to subsidies or preferential pricing, relative to smaller outlays. For some firms, it may be economically attractive to increase physical energy use in order to save costs on other productive inputs, for instance, by automating certain processes. Therefore, our results suggest that although management practices are associated with reduced energy intensity in value terms, it does not mean that physical energy use, and its related environmental impacts, will follow the same pattern.

Turning to focus on energy management practices, we find they have no additional effect on reducing the intensity of energy expense over and above generic management practices. It is possible, given the diversity of firms in our sample, that the coefficient reflects reverse causality: energy-intensive firms have stronger incentives to develop energy management. The decision to develop energy management practices may be the result of selection by subset of firms most likely to profit from them. Supporting this notion, we find that energy management scores are on average higher in energy-intensive sectors. Energy-intensive man-

> 17This is the title of
The number of FDPs is not small in terms of the absolute global poverty count. Before the COVID-19 pandemic, around 10 percent of the global population (780 million people) was estimated to be extremely poor, below 1.9 USD/day in purchasing power parity (PPP) terms. Using a conservative estimate based on empirical studies of about 1 in 2 FDPs being extremely poor, this translates into 41.2 million poor or about 5 percent of the global poor. The UN population division uses UNHCR’s FDP estimates to adjust national population figures by adding FDPs to population statistics. However, when the national and global poverty figures are estimated, FDPs are often missing from the poverty count.

Low interest, lack of proper microdata on FDPs, measurement issues and political reasons are some of the obstacles that prevent a proper measurement of poverty among FDPs. Before the Syrian conflict, poverty measurement among FDPs was largely confined to occasional exercises – often focusing on vulnerability rather than poverty – carried out by local or international NGOs on behalf of humanitarian agencies such as the UNHCR and the World Food Programme (WFP). These exercises often lacked the data sophistication and academic rigor that characterizes poverty measurement for regular populations in high-, middle-, or low-income countries, typically undertaken by development agencies such as the World Bank, UNDP or regional development banks. The UNHCR, for example, did not collect survey data on income, monetary consumption, or expenditure for refugees systematically as the focus and mandate of the agency is on humanitarian protection rather than poverty alleviation. It only started to collect data on income more systematically when budget restrictions forced the agency to start targeting cash programs. WFP did collect data on consumption regularly, but this effort was largely focused on food consumption for food security and nutritional assessments rather than poverty measurement. With little data and visibility, the economics profession and poverty specialists across the social sciences have also largely neglected FDPs (Verme, 2016; Dionigi and Tabasso, 2020). It is only in
In particular, we estimate the marginal effect of variables captured in household surveys on the probability of being enrolled in school in each country for youth ages 15 to 17.<sup>16</sup> We estimate a probit regression for each country using the latest household survey available, first focusing on individual (X), household (H), and community (Z) characteristics:

## ��������� ������� ����� ����� ��� (1)

Figure 15 plots the values of the marginal effects of gender and belonging to an indigenous group on the probability of attending, while controlling for all variables included in the specification, and confirms that in general, being female is positively correlated with being enrolled at secondary ages.<sup>17</sup> In El Salvador and Guatemala, however, the opposite is the case. This result is consistent with evaluation results from conditional cash transfer programs that have found that the gender gap in school enrollment has virtually disappeared in Latin America (Fiszbein, et.al. 2009).

Belonging to an indigenous group is in all cases negatively correlated with enrollment, with significant and sizeable effects in Guatemala, Honduras, Nicaragua, and Panama, with marginal

> 16 This is an approximation for dropout, as a small share of the youth who are not enrolled were never enrolled, and another small share may return to the schooling system in the future.

> 17 The (*) in this and the following figures denotes that the marginal effect is statistically significant at the 90 per cent level or above. Table A.3 presents the regression coefficients and standard errors depicted in Figures 15 through 17.

14
stagnant backcasted trend over the decade and in an estimated poverty headcount rate of 44.6 percent in 2009; the 2015/16-2018/19 GIC accurately captures the fact that richer Nigerians’ consumption is more sensitive to Nigeria’s growth. Overall, therefore, it appears that relaxing the assumption of a flat passthrough rate across the distribution shifts the backcasted estimate for 2009, but not enough to reproduce anything like the 17.3-percentage point drop implied by using 2009/10 HNLSS poverty estimate directly.

_Figure 5 Backcasting poverty rates assuming different pass-through rates across the distribution of household consumption_ <!-- Start of picture text --> 60.0<br>56.4<br>55.0<br>50.0<br>47.9<br>46.2<br>45.0<br>44.6<br>40.0<br>39.1<br>35.0<br>30.0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>HNLSS 2009/10 GIC 2010/11; 2018/19<br>GIC 2015/16; 2018/19 GIC 2010/11-2015/16<br><!-- End of picture text --> Note: the figure shows backcasted series using different values of the pass-through rate at different deciles of the consumption distribution. Household consumption data from the 2018/19 NLSS is matched to sectoral GDP growth rates (MFM-Tool World Bank) based on household head’s sector of employment. The backcasted poverty rates are calculated at the US$1.90 poverty lines. Different values of the decile-level pass-through rate are calculated using imputed household consumption data from three waves of GHS data (2010/11,2015/16, and 2018/19).

Lastly, we test the robustness of the results to using different methods to map the growth rates in sectoral GDP to the 2018/19 NLSS. In the
as distance to nearest bank need only be asked once, and this in fact has been done by administering a commune-level questionnaire, containing a section which identifies the main borrowing and savings providers, how far they are from the commune and what types of credit and savings opportunities they provide.<sup>15</sup> Note however, that the fact that only 2 per cent of communes are surveyed, a detailed geographical mapping of such information as mean distance to nearest credit institution for the households in a particular province is not available.

# _Other general household surveys_

General household surveys increasingly carried out by national statistical agencies outside of the framework of the LSMS can also contain useful information on financial services for much larger samples, allowing geographical and other breakdowns to be reliably considered. For example, the Tanzanian Household Budget Survey 2000-1 covered over 22,000 households. It covers education, economic activities and health status as well as household expenditure, consumption and income, ownership of durables and assets, housing, distance to services and food security. Under financial services, we learn whether the household had a bank deposit account, had received a bank loan in the past year, participated in a formal or informal savings group, and the distance to the nearest bank branch. Just five simple questions without any of the interpretative

> 15 The same is true for the Ghana survey where the distance to bank is asked at the community level.  Here about 300 enumeration areas were selected from a possible 13,000 stratified in order to achieve proportional representation of three main ecological zones in Ghana.   In Bosnia, a 6-way stratification of municipalities was determined before selecting 25 out of 146 urban and rura l municipalities to sample.

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|Treatment students|0.170***|0.100*|0.072|0.133**|0.260***|0.112**|0.240***|0.172***|
||(0.051)|(0.051)|(0.046)|(0.058)|(0.048)|(0.054)|(0.053)|(0.056)|
|Spillover students|0.028|−0.024|0.003|0.075|0.035|0.015|0.026|0.022|
||(0.052)|(0.051)|(0.044)|(0.058)|(0.048)|(0.051)|(0.052)|(0.052)|
|Control for baseline|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|value of dep. var.|||||||||
|Observations|4,390|3,864|4,389|3,896|4,344|3,854|4,391|3,906|

_Source_ : Authors’ analysis from student surveys.

_Note_ : Dependent variable is an index of related questions. All components of the indices are standardized to mean 0 and standard deviation 1, based on the sample frame at baseline. Standard errors clustered at the school level in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. _Condom use attitudes subindex_ : Respondent disagrees with statements (a) “It’s not right to carry a condom because people may think that I planned to have sex”; (b) “If a woman wants
Pierce, J. R., and P. K. Schott. 2016. “The Surprisingly Swift Decline of US Manufacturing Employment.” _American Economic Review_ 106(7): 1632-62.

Ramey, V. 2016. “Macroeconomic Shocks and Their Propagation _.” Handbook of Macroeconomics_ 2: 71-62.

Ramey, V. A. and S. Zubairy. 2018. “Government spending multipliers in good times and in bad: evidence from US historical data.” _Journal of Political Economy_ 126(2): 850-901.

Rose, A. K. 2013. “Protectionism isn’t Counter-Cyclic (anymore).” _Economic Policy_ 28(76): 569-612.

Santos-Paulino, A. U., and A.P. Thirlwall. 2004. “The Impact of Trade Liberalisation on Exports, Imports, and the Balance of Payments of Developing Countries.” _Economic Journal_ 114: 50-72.

Topalova, P., and A. Khandelwal. 2011. “Trade Liberalization and Firm Productivity: The Case of India.” _Review of Economics and Statistics_ 93(3): 995-1009.

UNCTAD. 1999. Trade and Development Report, UNCTAD, Geneva.

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and exports of goods as well as the number of international travelers. Although the health impact was less dramatic as initially feared, the impact on the economy was clearly visible.

Indeed, almost 60 percent of our sampled firms reported in the follow-up survey that they faced difficulties. About 58 percent reported problems in reaching out to their customers, 45 percent reported a decline in demand, 40 percent reported a problem in getting their supplies, and 13 percent reported problems with worker absenteeism (Figure 7).

## **Figure 7. Percentage of firms which encountered difficulties due to COVID-19 and government response measures**

_Source:_ Bagré Growth Pole Data Set.

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