rafmacalaba/data-use-mentions-extended huggingface dataset:
population with at least secondary education in the state, the population density (urban dummy). We select these variables based on the community variables employed in similar studies.<sup>9</sup> The climate change data ( is based on CCKP information at province/state level. Our study exploits monthly average temperature data from 1940 to 2022 by province from the CCKP (2023) database to II) approximate climate change signs over the natural temperature variability. We calculate a climate change signals index considering changes in temperature trends per time interval. By province, we estimate the monthly temperature average trend in 1940-2020 and 2000-2020. The index is the sum of the absolute differences between both trends over 2000-2020. A higher index indicates a more evident climate change signal. We also use precipitation data from CCKP by province. We compute the number of times in the period 1901-2021 that the z-score of the aggregated accumulated precipitation in mm is above or below two standard deviations.<sup>10</sup> And the rest of natural hazards variables at level 2 ( _N_ ) correspond to data from ThinkHazard! Platform, EM-DAT, and SPEI database which provide data at lower administrative geographic units than province level. However, it is essential to note that the sample representativeness of the survey does not apply to lower unit levels than province/state level. As a result, the outcomes are presented at either the province or country level.

From ThinkHazard! Platform, we use the index prepared by the platform that classifies hazards as high with 4 (potentially severe dangerous events), medium with 3 (potentially dangerous events), low with 2 (potentially dangerous events are less likely to occur but still possible), and very low with 1 (potentially damaging effects are unlikely to occur). ThinkHazard (TH) levels are derived from hazard maps applying the spatial distribution of hazard intensity (e.g., flood depth) at a given frequency or 'return period' based on a probabilistic analysis. We consider
.01, ** p<0.05, * p<0.1 _Notes._ Each cell represents a coefficient estimate using OLS regression relying on panel data estimation. The analysis is restricted to working age individuals in the years 2012 and 2018. In columns (1) to (4), the analysis is restricted to the sample of males. In columns (5) to (8), the analysis is restricted to the sample of females. The dependent variable is a dummy variable indicator equal to one if the individual is employed in the corresponding year 2012 and 2018 (according to the market definition of labor force participation). The main independent variable of interest is a dummy variable indicator equals to one if the individual reports that finding a job is one of the purposes of Internet use in the corresponding year 2012 and 2018. The regressions also include time-varying controls: age, a dummy variable for being married, three dummies for the individual’s educational attainment (primary or preparatory education, secondary education either general or vocational and above secondary education either post-secondary institute or university and above, and the reference category is no educational degree either illiterate individuals or literate individuals without any diploma), a dummy variable for rural areas as well as dummies for the various geographical regions included in the last column (Greater Cairo, Alexandria, and Suez cities, Urban Lower Egypt, Urban Upper Egypt, Rural Lower Egypt, and Rural Upper Egypt). The regressions also include individual and year fixed effects. The dependent variable mean is reported in the last row.

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# Measuring Disaster Crop Production Losses Using Survey Microdata: Evidence from Sub-Saharan Africa

Yannick Markhof *<sup>†</sup> Giulia Ponzini *<sup>‡</sup> Philip Wollburg *<sup>§1</sup>

**JEL Codes:** C81, C83, O12, O13, Q15, Q54.

**Keywords:** crop losses, disasters, crop production, agriculture, surveys, survey methods.

> 1 Authors listed in alphabetical order. † ymarkhof@worldbank.org, UNU-MERIT, United Nations University and Development Data Group, World Bank.<sup>‡</sup> gponzini@worldbank.org, Development Data Group, World Bank.<sup>§</sup> pwollburg@worldbank.org, Development Data Group World Bank. The authors would like to thank Shukri Ahmed, Joe Alpuerto, Piero Conforti, and Wirya Khim at FAO and Gero Carletto, Sydney Gourlay, Adriana Paolantonio, and Alberto Zezza at the World Bank for their comments and suggestions. This paper was produced with financial and technical support from the 50x2030 Initiative to Close the Agricultural Data Gap, a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030.
with certainty. We call this a “perfect loss” indicator. In response 8, we introduce an exclusion error of 25% to mimic the case of imperfect information.

The final two responses (9 and 10) combine the PMT indicators with the “perfect loss” indicator. We then distribute the fixed budget to all households ranked low according to each Proxy Means Test _or_ whose indicator shows they suffered a shock. By combining the two indicators we make sure that both the poor and those that suffered a shock receive support. The rationale behind using the perfect loss indicator and not the imperfect one is understanding how access to the best possible information during a crisis can improve proxymeans testing.

# 3. Data and methodology

For the microsimulations, in Albania and North Macedonia we rely on data from the Survey of Income and Living Conditions (SILC – 2018 for Albania and 2019 for North Macedonia). The SILC collects annual information on household incomes, including public and private transfers. It collects information about household’s annual income for the year before the survey, as such incomes for Albania and North Macedonia refer to the years 2017 and 2018, respectively. In Albania the total number of households covered was 772,197 and the number

> 13 This is a scenario where the number of individuals affected is known, but what remains unknown is which individuals were effectively affected or what the value of their loss is.

13
evidence in Bloom and Van Reenen (2007) and Bloom et. al. appoint a family member (especially the eldest son) as CEO are very badly-managed on average. Bennedsen et. al (2007) use the gender of a departing owner-CEO’s first-born child as instrument for patrimonial succession to support the causal nature of this relationship.

The evidence on the poor performance of family-managed firms therefore begs the questions: what causes firms to be family-managed, and what is the impact on aggregate performance? One potential explanation is that family management can substitute for weak legal structures, since trust between family members can be a substitute for weak contractual enforcement that can enable outside managers to expropriate resources from the firm (La Porta et. al. 1997, Burkart, Panunzi, and Shleifer 2003).<sup>3</sup> However, empirical evidence on this hypothesis is mixed both because previous efforts have focused on cross-country comparisons that are subject to omitted variable biases (La Porta, Lopez-de-Silanes and Shleifer 1999, Holmén and Högfeldt 2004, Roe 2003) and it is often challenging to disentangle family ownership from family management in the data.<sup>4</sup> This paper makes three contributions to our understanding of the causes and effects of family management. First, we create a unique database that offers a more systematic and comprehensive view of the incidence of family management and ownership than previously possible. La Porta, Lopez-de-Silanes and Shleifer (1999), with an eye to understanding the ownership structures of large firms, compiled data on largest 20 firms (by stock market capitalization) in 27 rich countries using the WorldScope database.<sup>5</sup> Addressing a broader spectrum of firms, the World Management Survey (WMS) (Bloom and Van Reenen 2007) advanced the literature significantly by asking explicit questions on ownership/management structure variables across a broader spectrum of firms, but overall sample size within and across countries remains modest.<sup>6</sup> as regional characteristics
and flash floods and small river basins have not been represented in a global model at this stage. These flood maps are overlaid with a global population density data set, Landscan (Geographic Information Science and Technology, 2015) to estimate the percentage of the population exposed to floods in each country, river basin, and for each return period.

An important complication is the difference between the exposure to a flood in a basin for a given return period (say, the 100‐year flood), and the number of people that are affected in the country _at once_ : while 2.1 percent of Brazil’s population is exposed to a 100‐year event in our simulations, this population is distributed in many river basins that will not flood at the same time. Here, we make the simplifying assumption that river basins in each country are independent, neglecting cross‐basin correlation (Jongman et al., 2014), and we perform a Monte Carlo analysis to derive exposure in a country at a given return period by aggregating simulations at the basin level.

We use estimations of the exposure bias published by the World Bank (Hallegatte et al., 2016a). The World Bank study overlays the same GLOFRIS flood maps with geo‐localized household surveys (from the Demographic and Health Survey) to assess the exposure of poor people relative to the exposure of non‐ poor people within 52 countries. Countries where data are not available are attributed the average exposure bias.

We proxy asset vulnerability and the asset‐vulnerability bias using a global data set of building types (USGS PAGER). We classify buildings in three categories (low, medium, and high quality), which we match to simple damage‐depth functions (Hallegatte et al., 2013), and we assume richer households live in and use higher quality assets. What fraction of assets is lost to a disaster also depends on softer measures, such as the existence of early warning systems. In addition to being an effective way of
At present, there are few effective sanctions to prevent the encroachment on commons by non-poor groups. Existing penalties under the OPLE are too low and are rarely enforced. Computerisation of the encroachment register at tehsil level will make possible the ready detection of habitual encroachers. Increasing the penalty according to the frequency of repeat encroachments may also act as some disincentive, although only over the medium-term rather than the short-term, and not so long as incentives towards rent-seeking remain strong for low-level revenue department officials.

The inefficiencies and distorted incentive structure that prevail in land revenue administration are only likely to be checked with greater awareness and voice on the part of local communities. In order for local groups to be able to press their legal claims, they first need to know what their rights are. A combination of awareness-raising and local organisational capacity-building, efforts to expand the effective powers and accountability of the panchayati raj institutions, increased vigilance of superior officers in the revenue administration, and an informed public opinion (e.g. through readily accessible manuals in local languages) are required in order to protect and promote access to remaining common land by the rural poor.

# 4.4 **Land tenancy**

The abolition of tenancy in order to vest land ownership with the actual tillers of the land formed the corner-stone of most land reform efforts in India after Independence in 1947. It was predicated on the widely (and then largely correctly) held belief that the tillers of the land were locked in exploitative relationships with intermediaries who had little interest in the land but for the extraction of rent. The Orissa Estates Abolition (OEA) Act, 1952 initially permitted landowners to resume 33 standard acres for personal cultivation (section 2.3), which led to the large-scale eviction of tenants. No parallel legislation was introduced to protect tenants. Under the Orissa Land Reforms (OLR) Act, 1960 (as amended in 1972), the leasing of land was explicitly banned, except where lessees fell into certain excluded categories<sup>36</
in outcomes between the two groups can be attributed to the state to which they were assigned to serve.<sup>16</sup> The randomization procedure is as follows: Each university submits a list of its graduating class to NYSC. This list includes the name, gender, marital status, date of birth, course of study, state of origin, and date of graduation. NYSC identifies those who are above 30 years old at graduation and issues them a certificate of exemption, as they are not eligible to serve. Married women and those with chronic illnesses are required to submit documentation in support of their status and are manually assigned to their family’s home state.

All other graduates are randomly assigned to the different states of the country using a computer algorithm. According to NYSC staff, the algorithm takes into consideration the following factors: meeting the manpower needs in different parts of the country, ensuring that no student is posted to his or her home state, and producing a cohort of corps members in each state that reflects the diversity of the overall graduating cohort in the country. NYSC sends back the list of students and their state postings to universities that then distribute to students.

There are two stages in which state assignments may depart from the randomization protocol. First, after students receive their assignment, they may request to be redeployed to a different state given the provisions for health or family reasons (genuine or otherwise), or they may wait until the next batch of postings to be remobilized. As such, the state in which an individual serves may be different from the assigned state. Matching the survey data and administrative data reveals that 87 percent of participants serve in their assigned state. Of the 13 percent that do not, only one individual redeploys from a Yoruba state

> 16Without the exogenous variation from NYSC assignments, it would be misleading to simply compare people living in their ethnic homeland to those who are not since different unobservable characteristics such as openness to other cultures, tolerance, ambition
data sources, we estimate the parameters of the model by maximum likelihood. Specifically, the contribution of the data for country c to the log-likelihood function is given by: (13) lnL(α, β, σ|y(c)) ∝ln|BΣB+ ββ<sup>′</sup> | + (y(c) −α)′(BΣB + ββ)<sup>−1</sup> (y(c) −α) Summing across all countries and maximizing with respect to the parameters delivers maximum likelihood estimates of α(k) , β(k) , and σ<sup>2</sup> (k) for the representative data sources.<sup>23</sup>

In the second step, we estimate the parameters for non-representative sources covering subgroups of countries with different geographical or income group coverage using a different approach. This is because we cannot assume that the unobserved distribution of governance has the same mean and standard deviation in these subgroups as in the global sample. To address the problem, in the second step we interpret Equation (1) as a linear regression and substitute unobserved governance g(c) with an estimate of governance based on the representative data sources, ĝ(c) = g(c) + u(c) where u(c) represents the measurement error in our estimate of governance based on representative data sources. Because the right-hand-side variable in this regression is measured with error, the slope coefficient will suffer from attenuation bias, i.e. for non-representative data source k , the probability limit of the slope coefficient of a regression of y(c, k) on ĝ(c) is plimβ̂(k) = β(k)<sup>V[g(c)]</sup> ~~.~~ However, V[ĝ(c)] because we know the margin of error of the estimate of governance based on the
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Bidder, R. M., J. R. Krainer, and A.
1577 firms, and reducing it to 1 (mean = standard deviation) reduces it to 395 firms. Based on this and experiences in other countries, my recommendation is therefore to aim for an initial pool of firms whose standard deviation of sales is no greater than their mean. However, as we have seen, the number of firms in any particular industry in most African countries can be quite small, so we are unlikely to get treatment and control groups of 300 firms each using such an approach. This brings me to the second step needed:

# **4.2 Collect a Lot More Data on These Firms**

The typical SME project collects very little data on the firms in the project, and often none on comparable firms not participating in the project. Even in cases where experiments are being planned, the default option is often to conduct a baseline survey, a midline survey (at say 1 year) in some cases, and then an endline survey. While such an approach is often appropriate for educational and health interventions in which outcomes like anthropometrics and test scores are highly persistent, but it is less well-suited to learning about dynamic firms, whose sales and profits can differ dramatically from one month to another due to seasonality, idiosyncratic demand shocks, shocks to the supply of labor, and a host of other reasons.

In McKenzie (2011), I provide theory and evidence to show the increases in power possible from collecting more waves of data than the usual baseline and follow-up. There are two important uses for more frequently collected data. The first, and better known, is for understanding the trajectory of program impacts, and for helping to unpack causal chains. For example, if we want to know whether an effort to formalize firms increases firm growth by allowing firms better access to formal credit, having multiple data points allows measurement of whether changes in credit precede firm growth or instead follow it. The second, and less practiced, use of more frequent data is to collect multiple measurements
bride price and the amount spent for the wedding as proxies for the bargaining power of the wife. Controlling for these variables, capturing in part the bargaining power of the wife, does not affect our estimation results, which makes us confident that assortative mating does not bias our results. Another interpretation of this potential assortative mating could be that women who choose to marry a man with a higher perceived risk of polygamy may be less averse to polygamy. In such a case, our results would be biased toward zero since we would expect less response to the anticipated risk of polygamy.

IV. DATA _The PSF Individual Panel Survey_ The data used in this paper come from an original nationally representative household survey, “Poverty and Family Structure” (PSF), conducted in Senegal from 2006 to 2012.<sup>16</sup> The data were collected in two waves, in 2006 and 2007 for the first wave and 16 The survey has been conducted by a team of French researchers and researchers from the National Statistical Agency of Senegal and is described in detail in DeVreyer et al. (2008). Momar Sylla and Matar Gueye (both of the Agence Nationale de la Statistique et de la Démographie of Senegal [ANSD]), and Philippe De Vreyer (ParisDauphine Dauphine, IRD-DIAL), Sylvie Lambert (PSE), and Abla Safir (World Bank) designed the survey. The data have been collected by the ANSD thanks to the funding of the International Development Research Center (IDRC), INRA Paris, CEPREMAP and the World Bank.

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# **Figure 2: Distribution of gap of survey to national accounts consumption**

_Panel A: Survey mean vs HFCE mean_ <!-- Start of picture text -->
-1  -.5  0  .5  1<br>Gap<br>All surveys  Income surveys  Consumption surveys<br>200  2.5<br>2<br>150<br>1.5<br>100<br>Density<br>Frequency  1<br>50<br>.5<br>0  0<br><!-- End of picture text -->

<!-- Start of picture text --> All surveys Income surveys Consumption surveys<br><!-- End of picture text -->
# **5 Methodology**

In order to quantify the impact of the influx of forcibly displaced on the fiscal and labor market outcomes of native workers, most studies use the share of population (or working age population) of the forcibly displaced as the independent variable and acknowledges that there exists a limitation in identifying the causal impact in this way because of the potential endogeneity arising from the sorting of forcibly displaced to areas with certain characteristics and other time varying factors, such as public expenditure, that may lead to spurious correlation. Many studies try to address these endogeneity issues by using instrument variable methods (Verme and Schuettler, 2021).

This paper tries to evaluate the effect of the forcibly displaced by using the discontinuity that is observed in the population data, mainly the working age population, in the first quarter of 2018, in a regression discontinuity (RD) design to evaluate the fiscal and the labor market effects.<sup>8</sup> In the RD design, this paper uses a transformed variable (X) of the time quarters as the running or score variable. X is the distance between any time in quarter and the the first quarter of 2018. Since the PNADC data is from 2012-2020, there are 36 observations for X, with X being equal to zero in the first quarter of 2018, which is defined as the cutoff. The treatment is defined as when _X >_ = 0 and is denoted by _Di_ = _I_ ( _Xi >_ 0), where _I_ ( _._ ) is the indicator function. Since the fiscal data is monthly, the cutoff variable is the second bimonth of 2018 and X is defined as the distance in 2 months from the second bimonth of 2018. It should be noted here that assigning to the treatment condition does not mean receiving the treatment. For example, in this case, the Venezuelan refugees and migrants started coming in to Brazil in 2017, but the net influx did not become substantial till the first quarter of 2018,