rafmacalaba/data-use-mentions-extended huggingface dataset:
reduce productivity and female well-being more broadly.<sup>3</sup> In fact, reforms to equalize women’s inheritance rights in Kenya -albeit not applied equally everywhere (Linkow, 2019)- had multiple positive impacts on women’s livelihoods (Harari 2019).

While the above implies that forestalling gradual erosion of women’s land rights could be an important policy issue in customary tenure systems, gathering data to measure women’s rights and tenure security is likely to require considerable effort. Contrary to formal systems where the type of right associated with each document is defined in law and holders’ names are written down, household surveys conducted in countries dominated by customary tenure systems ought to elicit information on whether the rights to bequeath, sell, rent out, use as collateral, make improvements are held individually or jointly by different individuals for each parcel (Doss et al., 2019). While there is little doubt that asking all adult individuals in private about their personal ownership of and rights to assets is the most desirable and recommended approach, the cost of doing so led most large-scale surveys to obtain this information through proxy reporting and specifically through the use of _most knowledgeable_ household members. Little is known about the magnitude of errors potentially associated with this practice.

# **2.2 Nature and relevance of land rights in Malawi**

Malawi is one of Africa’s poorest and most densely populated countries with an economy that is heavily dependent on agriculture. Historically, land allocation in Malawi has been dualistic. Land for cultivation of commercial crops was provided to estates under freehold or leasehold (Deininger and Xia 2017) while smallholders were left to produce food crops, mainly maize, under customary tenure. Although restrictions on small farmers’ participation in cash crop cultivation were eliminated in the 1990s, smallholders’ limited resources (Ricker-Gilbert _et al._ 2014) and vulnerability to risk, particularly in the face of climate-related shocks (Sesmero _et al._ 2018), constrained their ability to
firms are surprisingly less prone to innovate when compared to domestic companies: in the case of process innovation for instance, foreign companies are 31.6 percent less likely to innovate than domestic peers (column 4). Size of locality also plays an important role: being located in large cities increases the probability of innovating; for certain types of innovation activities (precisely for process, organizational and marketing innovation) this association seems to be nonlinear. Sector of activity also matters: all else equal, other service sector companies present the highest probability to introduce a product innovation (2.415 times higher) when compared to the baseline category (wholesale and retail).

Innovation inputs - defined as the set of investments and activities that firms carry out to develop their capabilities to innovate (Cirera, Lopez-Bussols and Muzi, 2016) - represent another relevant dimension to be considered when assessing innovation performance at the firm level. ES data cover a limited set of innovation inputs: R&D activities; purchase and/or licensing of (patented and non-patented) technology; and, formal training for development of innovative products/processes.

R&D activities represent the most commonly discussed innovation input in the literature; see for instance, Mohnen and Hall (2013) for a recent review of studies across OECD countries. ES data for the selected set of developing East Asian countries under analysis show that R&D intensity (measured as the total R&D expenditures per worker) does not vary too much with income level, a result that is driven by Indonesia which presents a low level of (average log) R&D intensity (Figure 13). The linear association (between R&D intensity and GDP per capita) would be strongly positive if Indonesia is excluded. On the other hand, R&D incidence decreases sharply with income level, a result that is robust to the exclusion of Indonesia. When taken together, these results suggest that - at least for this selected set of developing East Asian countries,
_Figure 1: Energy spending shares (income-based) and energy poverty rates (10 percent threshold) by income quintiles (in percent, 2019):_ <!-- Start of picture text --> 100.0<br>78.4<br>80.0 71.3<br>56.0<br>60.0<br>37.3<br>40.0<br>21.9<br>20.0 18.1 15.7 13.6 10.5<br>7.7<br>0.0<br>Q1 Q2 Q3 Q4 Q5<br>Energy spending share Energy poverty (>10)<br>Percent<br><!-- End of picture text --> _Notes: The graph shows the energy spending share in overall household income at the household level (in percentages) by income quintiles in Blue and the related energy poverty rates in Green. Energy poverty rates use a 0.1 threshold by income quintiles. Energy spending shares are income-based. Income quintile 1 is the 25 percent of households with the lowest average per capita income, while income quintile 5 is the 25 percent of households with the highest average per capita income. We weigh each observation by household survey weights. N=2,952._ _Source: Own estimates based on HBS (2019)._

**We impute energy spending shares from the HBS 2019 into the EU-SILC 2020 (with the income year**

**2019).** Ultimately, we are interested in generating information on energy poverty for households sampled in the EU-SILC. While we could also impute energy poverty indicators directly, imputing energy spending shares gives us the flexibility to explore several expenditure-based measures at once because these measures directly result from energy spending shares.

# **One important caveat is that energy poverty indicators that take a purely monetary perspective**

**suffer from conceptual limitations.** Energy
**severe material deprivation**<sup>**13**</sup> **among this group.**<sup>**14**</sup> Given the importance of distinguishing between static and chronic disadvantages, we use the EU‐SILC rotating panel in Bulgaria to identify and profile the chronic monetary poor. Given the availability of ethnic identifiers, we test whether the Roma population is more likely to be overrepresented among the chronic poor and identify whether they are more likely to face overlapping deprivations than the general population. This analysis aims to shed light on these marginalized communities' complex challenges and identify potential interventions to address their multifaceted needs.

**The first two parts of our study contribute to the existing literature on multidimensional poverty within the EU** . Alkire et al. (2014) employ the Alkire Foster (AF) methodology (Alkire and Foster, 2011) to introduce a set of experimental indices aimed at measuring multidimensional poverty in the EU, leveraging cross-sectional data from the (EUSILC) for 20062012. Notably, the study adapts the at-risk of Poverty or Social Exclusion (AROPE) indicator, enhancing its applicability within the EU context under the EU2020 framework. This index encompasses various dimensions beyond income, including employment, material deprivation, education, environment, and health. This work serves as a cornerstone for understanding multidimensional poverty in the region and is particularly relevant to our analysis. WeziakBialowolska and Dijkstra (2014) adopted the UN-Multidimensional Poverty Index (MPI) framework to construct a regional poverty index at the sub-national level in the European Union, using EU-SILC data spanning from 2005 to 2011. Their index considers three dimensions: living standards, education, and health. Deutsch et al. (2015) analyze cross-country variations in material deprivation in the 27 EU countries using latent trait analysis. Buligescu (2019) examines multidimensional poverty through the lens of various deprivation dimensions, focusing only on Romania and using data from the 2016 Household Budget Survey (HBS). Notably,
# **Table 5: First Stage Regression Results**

Dependent variable: Ln (Freight Rate)

||Control 1|Control 2|Control 3|
|---|---|---|---|
||(1)|(2)|(3)|
|Av. VAT rate at next probable destination|5.557***|5.191***|5.567***|
||(1.405)|(1.410)|(1.401)|
|Ln (Av. VAT rate in own route*Crude price)|0.109***|0.101***|0.115***|
||(0.025)|(0.026)|(0.026)|
|Intercept|-1.468|-3.491**|-1.802|
||(1.664)|(1.756)|(1.653)|
|No. OD pairs|23288|13678|23288|
|No. Absorbed FEs|1080|1003|1080|
|R-squared|0.514|0.514|0.514|

Note: Each column represents a coefficient estimate from separate regressions. The column heading corresponds to the set of controls for regressions results reported in that column. Control 1: ln (distance between origin and destination (OD)) and ln (average VAT in potential next destinations*crude price). Control 2: Control 1 plus ln (diesel price/litre along OD route). Control 3: Control 1 plus ln (average nightlight luminosity along OD route). The sample for column 2 is smaller because diesel price data is available only for 2019. Standard errors are clustered at the destination level. Robust standard errors in parentheses. *** p<0.01, ** p<0
technology diffusion might also occur due to a labor turnover from multinationals to domestic firms or if locals establish their own business (Djankov and Hoekman 2000).

Unlike most of the microeconomic evidence, studies using aggregated FDI data found questionable support that FDI in itself significantly boosts growth rates in all recipient

> 1 Much of the earlier literature has emphasised differences in capital abundance and returns on capital as the main driving force for capital flows across countries. See Caves (1996) for a literature survey. 2 De Mello (1997) and Li and Liu (2005) provide surveys of the literature. See also Görg and Greenaway (2004) for a review of the empirical literature on multinationals and productivity spillovers.

3
long-term impacts on wealth, spanning generations (Damania et al., 2017; Carrillo, 2020; Aguilar & Vicarelli, 2022; among others).

Over the last 50 years, the Latin America and the Caribbean region has had a rise in natural disasters and extreme weather events, as reported in the Emergency Events Database (EM-DAT) of the Université Catholique de Louvain, Belgium (Guha-Sapir et al., 2022). These events have resulted in fewer deaths over the years but higher damage costs, especially in the Caribbean islands when scaled by GDP. This study aims to provide a comprehensive analysis of the vulnerability to poverty in a climate risk framework. It seeks to estimate the number of people in the region who are at risk of being pushed into poverty due to climate disaster risk such as floods, droughts, or extreme temperature events.

In our analysis, we simplify the climate disaster risk by reducing dimensions and treating it as a type of covariate risk. We do this by using natural disasters that have occurred, extreme weather conditions that households have been exposed to, and natural hazard geographical indexes. We model the per capita household income and identify poor households within the vulnerability to poverty framework. The framework considers climate disaster risk, idiosyncratic risk, community factors, and specific household characteristics. The climate disaster risk is interpreted as an aggregate shock, while idiosyncratic risks are specific shocks to each household. Both risks are modeled through the shape of the per capita income distribution, estimating the expected variance. On the other hand, the expected mean income is estimated based on the household and community characteristics. It offers a more structural idea of the proportion of households already poverty-induced independent of the type of risk they face. This framework has been proposed by Chaudhuri (2002, 2003) and modeled by Gunther & Hartgen (2009) following Hox (2010) and used by Skoufias et al. (2023) for Ethiopia and Rud and Robayo-Abril (2023) for El Salvador.

However, our analysis differs and expands
**Table 3. The Informal Sector and Other Determinants of Economic Growth** (t-statistics in parenthesis)

|Dependent Variable|Grow|th Rate of R|eal Per Capi|ta GDP (198|0-92)|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Size of the Informal Sector|-1.2482|-1.3165|-1.3303|-1.4555|-1.2245|
||(-3.33)|(4.30)|(-3.79)|(-3.45)|(-3.44)|
|Real Per Capita GDP, 1980|-0.6418|-0.7956|-0.8906|-0.1523|-1.9685|
||(-2.04)|(-2.92)|(-2.80)|(-3.84)|(-4.36)|
|Secondary School||0.4302|0.7341|0.8426|0.7118|
|Attainment, 1980||(1.86)|(2.32)|(2.66)|(2.34)|
|Average Tariff for|||-0.5226|-0.6138|-0.3439|
|Intermediate and Capital<br>Goods, 1985|||(-1.26)|(-1.46)|(-0.86)|
|Average Inflation Rate,||||-0.2497|-0.6027|
|1980-92||||(-0.46)|(-1.60)|
|Public Infrastructure Index,<br>1990
as a constraint is 13.3 percent, with the corresponding figure for business licensing and permit being 7.7 percent. Given that it is not possible to evaluate the accuracy of perception-based questions, all the analysis can indicate is the presence or lack of stability in the measures, but not the direction of the bias. Question order seems to have no statistically significant effects on experience-based or objective measures of corruption and business licensing and permits.

A key insight from this study is that wherever possible researchers should complement the use of questions on business environment perceptions in the WBES with more experience-based measures that trace the specific situations and transactions the firm undergoes. This is particularly the case of corruption and business licensing and permits. On a more positive note, 11 of the 15 business environment elements do indicate some level of stability in their perceptions. We cannot state with certainty why perceptions of corruption or business, licensing and permits are susceptible to change depending on their placement in the survey instrument, but we provide some conjectures. Corruption is a highly sensitive topic. It is quite likely that a respondent has strong priors about it before the survey. Furthermore, one can imagine situations where businesses are nervous about the government institutions in their economies. Such anxiety and perceptions could evolve through the questionnaire as the respondent is queried about different aspects directly and indirectly related to transactions with the public sector.

This study brings into light the question on how robust or stable perception-based or attitude questions are for firm-level surveys. Future work should explore ways by which the stability of perception-based measures can be increased. We also acknowledge that the findings may only apply to the Venezuelan private sector. Although the firms surveyed were not in the extractives sector, the nature of the Venezuelan economy may be unique, and its dependency on extractives may influence several aspects of the private sector including its size and influence. Thus, further survey methodology experiments in other countries would help ascertain the external validity of these findings.

18
col-**<br>**leagues**|**Corruption**<br>**supervi-**<br>**sors**|**Ethical**<br>**behavior**<br>**is sanc-**<br>**tioned**|**Receives**<br>**threats**|**Promotions**<br>**are fair**|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Average signifcant excess interaction|2.239|0.364|4.033|-0.053|3.267|1.606|
||(2.068)|(2.078)|(2.506)|(2.221)|(2.567)|(2.124)|
|Observations|20|20|20|20|20|20|
|R-squared|0.061|0.002|0.126|0.000|0.083|0.031|

_Notes:_ Standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. _Average significant excess interaction_ is measured as the share of declarations handled by the inspector that were subject to significant excess interaction calculated using calibration methods (as explained in Section 4). The dependent variables are taken from a nationwide survey of inspectors conducted in 2017, typically scored from 1 to 5 (see Table A1 part H for a detailed description of the variables).The sample covers the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention).

90
and 27 _μg/m_<sup>3</sup> , respectively. Smoke plumes during the year 2009 did not contain density information and were removed from analysis. Since crop type data starts in 2008 (Section 4.1.2), our final smoke plume dataset spans 2008–2020, excluding 2009, and includes 332,547 plumes total. Of these, 250,406 are low density, 58,847 medium density, and 23,294 high density. Due to the small number of high-density smoke plumes, we grouped medium- and high-density smoke plumes together and refer to this group as high-density smoke in this paper.

From these raw plumes, we created daily smoke exposure polygons over the growing season. We first took the union of all low-density smoke plumes at daily resolution from May 1 to September 30. We did the same for all high-density smoke plumes. We then subtracted the footprint of high-density smoke from low-density smoke daily, because low-density smoke plumes are often drawn in ways that include high-density smoke (i.e. they may represent _at least_ low density smoke). Since smoke plumes were aggregated to daily temporal frequency, if a given area is under a low-density smoke plume early in the day and then under a high-density smoke plume later, we classified it as being exposed to a high-density, and not a low-density, plume for that day. These steps were performed in `Python` using the `geopandas` package.

Lastly, we rasterized the daily plume polygons into images at 30m resolution, using the same projection as our crop type maps (Section 4.1.2). Every day has two rasters, one for low smoke and one for high smoke. Each pixel of a raster has value 1 if a smoke plume covered that pixel that day, and value 0 otherwise. We summed the daily images to obtain one final raster for low and high smoke, where each pixel represents the total number of days during the growing season that the pixel was exposed
(7), which deliberately suppresses any discussion of internal demand conditions in developing countries. To the extent that we incorporated demand considerations, this was through a measure—changes in demand conditions reported by the Federal Reserve—which proxies for external demand conditions, mainly from high-income countries. While we certainly recognize that (7) does not fully account for internal loan demand, our preference for this approach—at least at the outset—was conditioned by a desire to focus which transmission channels affect the supply side, along with the desire to introduce interaction effects between the crisis and the main coefficients of interest ( _β_ 1, _β_ 2, and _β_ 3).

In this section, we substitute the generic measure of demand conditions with country-specific interest rates. Accordingly, (7) can be re-expressed as where _rit_ is the domestic lending rate in country _i_ at time _t_ , which now more fully approximates the potential effect of local demand for bank loans. Of course, since interest rates are codetermined by bank lending as well as saver deposits, _r_ is endogenous. Consequently, we require an instrument to identify the loan demand function, and (8) is estimated via instrumental variables (IV). For this purpose, we rely on country demographics; in particular, we exploit the 18
-0.55)|
|If phone interview|||-0.0499*|(-1.88)|||-0.0353|(-1.05)|
|Othervariables and school fixed effects|yes||yes||yes||yes||
|Percentage of predicted values out of|||||||||
|<br>[0,1]|5.10%||5.00%||6.60%||6.20%||
|Number of observations|2,962||2,962||2,761||2,761||

Source: TEEP Tracking Survey. Notes: Other variables include house value in 2000, residential land value in 2000, commercial land value in 2000, and agricultural land value in 2000. t values are in parentheses.

* significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

39
multiple (‘intersecting’) factors across multiple levels of society need to be considered, and the way they are interconnected (Yacob-Haliso, 2016; Kapilashrami and Hankivsky, 2018; Agbonifo, 2020). However, empirical evidence from such intersectionality- and gender-based analyses of conflict settings remains scarce, especially micro-level, quantitative evidence. We contribute such evidence by studying quantitatively how forced displacement, gender and violence jointly shape crisis responses at three levels of observation (the household, adult and child levels).

Second, we contribute to a better understanding of conflict crises. Conflict crises present affected populations with various forms of adverse conditions. However, how different (groups of) people experience these conditions is likely to be highly heterogeneous but under-researched (Brück et al; 2016). Coping behaviors in particular are at the core of how individuals experience and survive acute conflict situations (Lautze, 2006). Yet, high-quality large-N quantitative information on conditions, exposure, and behaviors in these contexts is scarce, as collecting it is inherently difficult, due to access, safety and ethical concerns (Kolade et al., 2019; Sarzin, 2017). At the same time, micro-level data and evidence are key to understanding conflict outcomes and impacts (Canetti et al., 2017; Bohnet et al., 2018; Hartman et al., 2021; Canavan and Turkoglu, 2022), as well as informing policies to protect and support lives and livelihoods in conflict situations. Our study matches high quality survey and conflict event data to produce disaggregated insights into how households, adults and children react to the complex challenges conflict crises entail.

The remainder of the paper is organized as follows. Section 2 describes the context of our study. Section 3 presents our methodological approach. Section 4 introduces the data and provides 3
above-average income group. For this income group housing is a pressing concern that could motivate them to turn to market methods. At present higher-income households that are not satisfied with their housing conditions do not see much sense in privatization and are biding their time before exercising their right to privatize their residences. Sociological surveys (Guzanova, 1994) show that it is this category that is extremely eager to own dwellings, including expensive country houses. Their seemingly "indifferent" behavior during privatization does not mean that they are opposed to private ownership of housing; rather, under current housing policy they have practically no opportunity to obtain better dwellings. As a result, privatization cannot help them. In the meantime, under other circumstances private housing could become a springboard for the purchase of new dwellings. However, there is no clearly defined procedure that would facilitate such purchases. Mortgage credits are, for all practical purposes, non-existent. Confidence in realty companies is low. And the taxation of the sale and purchase of housing does nothing to promote the housing market (Nozdrina, 1995).

As a result, the rates of housing privatization in small towns recently exceeded those in Moscow, where the process took off earlier, the share of private housing is slightly over 40%/o, and privatization has not appreciably progressed in the last year. The reason is probably the fact that the housing market is out of reach for most people living in big cities: the prices are too high, and supply is too low.

The high rates of privatization of housing in smaller towns show, or so we believe, that their residents have broader opporunities to improve their housing situation (we deal with this issue in more detail in the Section 2). Residential mobility in such towns is indeed high and, in contrast to Moscow, is typical of all income groups, which indicates normal exchange of housing between poor and wealthy families. Also important are the more affordable prices (as compared to Moscow), as well as a wider choice of