rafmacalaba/data-use-mentions-extended huggingface dataset:
treatment effects are presented in Appendix C.

The main outcomes of interest in the randomized survey experiment relate to respondents’ support for subsidy reform. This is measured individually for each of the outcome questions and collectively by aggregating the results for the questions about unconditional and conditional support into two indexes (unconditional support index and conditional support index). Specifically, using an approach similar to that used in related randomized survey experiments by Alesina et al. (2018) and Karadja et al. (2017), indexes are created that are an unweighted average of the z-scores of the outcome variables, oriented so that a higher index means more support for reform. The answers to each outcome question and the unconditional and conditional indexes are presented in Tables 4, and 5.

Three main types of experimental analysis are conducted. The first tests H2 by examining if providing any information (i.e., pooling all three treatments) about the negative aspects of energy subsidies shifts support for reform (across all countries). This is examined by conducting pooled (across treatments and countries) Ordinary Least Squares (OLS) regressions with country-fixed effects with a dummy variable that takes on the value of 1 if the respondent belongs to any treatment group and the value of 0 if the respondent belongs to the control group. This approach is in line with what was undertaken by Alesina et al. (2018, 2022). This analysis can be expressed formally as follows: where _i_ denotes individuals, _j_ denotes countries, and _β_ 1 captures the average difference between respondents in any treatment group ( _T_ ) and the control group about the outcome of interest ( _Y_ ). Further, _Xi_ is a vector of variables that controls for potential imbalances in background characteristics (age, sex, location, income, education level, perceived relative income, actual income, energy consumption, trust in government, belief in government capacity, knowledge of subsidies) between treatment and control groups, _θj_ captures country fixed effects, _εij_ is the model error term (clustered
# **Abstract**

A range of alternative empirical definitions of informal activity have been employed in the literature. Choice of definition is often dictated by data availability. Different definitions may imply very different conceptual understandings of informality. In this paper, Henley, Arabsheibani, and Carneiro investigate the degree of congruence between three definitions of informality based on employment contract registration, social security protection and the characteristics of the employer and employment using Brazilian household survey data for the period 1992 to 2001. 64% of the economically active are informal according to at least one definition, but only 40% are informal according to all three. Steady compositional changes have been taking place among informal workers, conditional on definition. The econometric analysis undertaken by the authors reveals that the conditional impact of particular factors (demographic, educational attainment, family circumstances) on the likelihood of informality varies considerably from one definition to another. Their results suggest growing heterogeneity within the informal sector. Therefore, the authors argue that informal activity may be as much associated with entrepreneurial dynamism as with any desire to avoid costly contract registration and social protection. However, this paper confirms that there is no a priori reason for entrepreneurial activity to be unprotected. Consequently definitions of informality based on occupation and employer size seem the most arbitrary in practice even if conceptually wellfounded.

2
Policy Research Working Paper 10660

# **Abstract**

Disasters affect millions of people each year and cause economic losses worth many billions of dollars globally. Reporting on disaster impacts in research, policy, and news primarily relies on macro statistics based on disaster inventories. The macro statistics suggest that a relatively small share of disaster damages accrues in Africa. This paper, instead, uses detailed survey micro-data from six African countries to quantify disaster damages in one key sector: crop agriculture. The micro-data reveals much higher damages and more people affected than the macro statistics would indicate. On average, 36 percent of the agricultural plots in the sample suffer crop losses due to adverse climatic events. In the countries and time period analyzed, these losses reduced total crop production by an average of 29 percent. Importantly, many of these losses are underreported or undetected in key disaster inventories and therefore elude macro statistics. In the case of droughts and floods, the economic losses recorded in the micro-data are $5.1 billion higher than in the macro statistics, affecting 145 million to 170 million people, more than four times as many as the macro statistics suggest. The difference stems mostly from smaller and less severe but frequent adverse events that are not recorded in disaster inventories.

This paper is a product of the Development Data Group, Development Economics. 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 pwollburg@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
**Table 1:** This table shows the livestock type with the conversion factor. Sources: Eurostat (2018)

|**livestock type**|**conversion factor**|
|---|---|
|Cattle|1|
|Pigs|0.3|
|Goats|0.1|
|Sheep|0.1|
|Chicken|0.01|

of the animal equivalent values are multiplied by pixel areas to get the count of animals per grid, which is multiplied by the FAOSTAT’s value of production to obtain the livestock production prior for each pixel.

where _lsvali_ is the total value of livestock production in pixel i; _lsvalx_ is the value of livestock production (meat, milk, eggs, honey and wool) that is reported at the national level; _lsnumi_ is the total number of equivalent animals in pixel _i_ ; and _X_ is a set including all pixels that fall within the boundary of a nation.

A map of global gridded livestock production value as a prior is shown in Figure 2.

# **2.1.3 Forestry production and hunting**

Forest resources have been utilized by people since the advent of civilization (Hossain, Alam, Miah, et al., 2008). Up until now, over a billion people still rely on forest resources for food security and income generation to some extent (FAO, 2018). In the world’s least developed regions, 34 countries depend on fuelwood to provide more than 70% of energy, among which 13 nations require 90% of energy (FAO, 2018).

The contribution of forest production to AgGDP can be classified into two broad types: wood (logging) products and non-wood forest products. Wood (logging) products are the most exploited commodities in the forestry sector. The trees are cut down to be the raw materials for producing timber and pulp, which are further processed and converted into a number of derivatives, such as construction materials
differentials between PLs accurately reflect regional price differences for goods in the CBN basket. Based on our assessment this does not appear to be the case.

We began by analyzing price differences between urban Maputo and rural N-CD using a Paasche index<sup>20</sup> constructed based on survey prices, which indicates a differential of 18 percent; a Paashe index based on SIMA<sup>21</sup> prices (which does not differentiate between rural and urban Maputo) produces a 30 percent differential. Similar calculations performed elsewhere (Sohnesen, 2011) put the price-index ratio between 5 and 20 percent.22 As a further robustness check, in computing the Paasche indexes we used only those observations that were 20 percent below or above the PL, in order to see whether the differential increased the closer we moved to the PL. The Paasche index based on both food and nonfood items from the household survey registers a differential of 30 percent provinces; based only on food the differential is just 5 percent, and the Paasche index based on SIMA prices produces a differential of 16 percent. Similar results are obtained when only observations below the 55th percentile<sup>23</sup> are used. In all cases, the observed price differentials are dwarfed by the vast differences in regional poverty lines.

The rural areas of Maputo and Nampula are more directly comparable, yet here as well the considerable PL spread (73 percent) is not justified by any similar difference in prices. Sohnesen’s simulation indicates a price differential of between 31 and 40 percent, while our deflator (whether based on survey prices or SIMA prices) indicates little or no difference between the two areas. As in the previous case, we compute the Paasche index ‘for the poor’, and here the differences are minimal: 13 percent for the food and non-food index, 20 percent when using only food items, and a negative differential of 2 percent when using SIMA prices.

To summarize, these findings suggest that the spatial distribution
/year)|P-<br>value|Indicative<br>premium,<br>with<br>detrending|Percentage<br>premium<br>reduction due<br>to detrending|
|---|---|---|---|---|---|---|
|Gujarat|Cotton|17.4%|111|4.8%|6.2%|64%|
|Maharashtra|Cotton|17.3%|62|0.4%|3.1%|82%|
|Karnataka|Cotton<br>(irrigated)|8.5%|28|2.4%|3.1%|64%|
|Andhra<br>Pradesh|Cotton<br>(irrigated)|10.5%|80|0.2%|1.6%|85%|
|Gujarat|Groundnut|26.6%|83|32.7%|n/a|n/a|
|Gujarat|Pearl Millet|17.4%|40|17.1%|n/a|n/a|

Note: Premiums are commercial premiums rates, based on the existing NAIS normal theory method without detrending, and using an indemnity level of 60% and within-state weighting by 2007 area sown.

Source: World Bank (2011a), based on data from AICI.

For risk classification to be possible, there must be flexibility to determine either premium rates or threshold yields on an actuarial basis, that is using statistical analysis of past experience, incorporating expert opinion and allowing for noticeable trends in past experience. Under the NAIS, neither the premium rate nor the threshold yield is flexible, but rather determined using simple formulae, and in practice there is significant inequity within states.

For political economy and administrative reasons, varying the threshold yields across the state may be preferred over varying the
- <mark>Milanovic, B. 2021. “After the financial crisis: the evolution of the global income distribution between 2008 and 2013.”</mark> _<mark>Review of Income and Wealth</mark>_ <mark>. h</mark> ttps://doi.org/10.1111/roiw.12516 .

- <mark>Narayan, A., Cojocaru, A., Agarwal, S., Bundervoet, T., Davalos, M., et al. 2022. COVID-19 and Economic Inequality: Short-Term Impacts with Long-Term Consequences. Policy Research Working Paper No. 9902. World Bank, Washington, DC. .</mark>

- <u><mark>https://openknowledge.worldbank.org/handle/10986/36848</mark></u>

- <mark>National Bureau of Statistics of China. 2021. “Households' Income and Consumption Expenditure in 2020.” Available at</mark>

<mark>http://www.stats.gov.cn/english/PressRelease/202101/t20210119_1812523.html.</mark> <mark>National Bureau of Statistics of China. 2022. “China Statistical Yearbook 2021.” Available at http://www.stats.gov.cn/tjsj/ndsj/2021/indexeh.htm.</mark>

- <mark>Office of National Statistics. 2022a. “The effects of taxes and benefits on household income, disposable income estimate.” https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/i ncomeandwealth/datasets/householddisposableincomeandinequality</mark>

<mark>Office of National Statistics. 2022b. “Households: Real disposable Income, per head: Quarter on Quarter-1 growth %: Chained volume measure £: Seasonally adjusted.” https://www.ons.gov.uk/economy/grossdomesticproductgdp/timeseries/crxz/ukea Palomino,
<mark>income poverty. One result that has emerged from this literature is that female‐headed households are extremely heterogenous and so reliance on the correlation between the sex of the household head and poverty rates is often misleading. The route to becoming a female household head reflects a variety of circumstances, including for example, widowhood, divorce, desertion and separation, which entail different degrees or complete absence of choice about becoming a household head (Chant, 2003, 2008). Women’s access to labor markets and assets varies according to social class and income. Also, the means to meet household needs include the “moral economy” of community and kinship rules (Kabeer 1997) and these rules may accord different entitlements to female household heads for example, widows versus single mothers, depending on social norms. So, whether female headed households are disproportionately represented among the poor or not will depend on the circumstance leading to their headship as well as the broader social context, including the access to labor markets and assets, prevailing</mark> social norms, their kinship‐based claims and other entitlements.

<mark>The UNHCR data identify principal applicants for each household and our analysis examines differences in household poverty between households with a female rather than male principal applicant.</mark><sup>6</sup> <mark>The principal applicant is the person who receives assistance from UNHCR for the family and is self‐selected or selected by the family. This definition of female headship has advantages over the way that household headship is commonly identified in household surveys. An often‐noted drawback of the headship variable is that female headship may reflect the enumerators’ perception about who should be considered a family head rather than who has the most responsibility for the family’s welfare in practice.</mark><sup>7</sup> <mark>Social norms can also affect whether female respondents self‐identify as household heads. For example, some Eritrean returnees who would
50 question of vho uses vhat services. Akin and Schvartz (1988) have undertaken a sultivariate analysis for Thailand and Jamaica that controls for most of the determinants of contraceptive source choice, and the results of this analysis are drawn upon in each of the subsections to place the importance of each variable in some context.

Location. Government is usually the most important contraceptive supplier for rural'vomen. Table III.3 summarizes source of contraception by urban and rural location. In every country but Zimbabwe, government serves a majority of rural contraceptive users. In the Asian and African countries included in the table, government is also the predominant source of contraceptives in the urban areas. In Latin America and the Near East, commercial sources serve close to half or more of all urban users. NGOs outside of Africa are more important as contraceptive providers to urban areas, vhereas the African NGOs are more likely to meet the modern contraceptive needs of rural households.

More focused evidence from Chogoria Hospital in rural Kenya indicates the relative success of NGO programs in family planning. Although the full costs of the program are not known, Chogoria achieved a contraceptive prevalence among married vomen of reproductive age of 34 in 1984, as opposed to the 10 percent figure for the nation as a vhole that emergel from the 1984 Kenya Contraceptive Prevalence Survey. Family planning services are free at both government health posts and Chogoria Hospital's 26 clinics (Chogoria Hospital Annual Report, 1986).

Government is fulfilling an important function of meeting the contraceptive needs of rural vomen outside of Africa. If commercial sources are in short supply in rural sections of these countries, then public investment may be critical to use. Hovever, the available data do not allov any firm conclusions, although on average incomes tend to be lover in rural areas and therefore subsidies are likely to be more needed and varranted.

a1.fp:VavAly **Planning Pap t (Ao1o)**
assigned to a corrupt official were removed, aggregate output and mean plant size would grow by 63.6% and 82.5% respectively.

The rest of the paper is organized as follows. The next section provides a concise literature review. Section 3 describes the data sets used and summarizes descriptive facts related to bribery and plant size in the US and Türkiye. Section 4 presents the model and derives key equations to compare the different steady-state equilibria. Section 5 describes the calibration of the parameters and discusses the model’s behavior with the calibrated values. Section 6 presents counterfactual experiment results, and Section 7 concludes.

3
19.7|45|55|46|
||No labor earnings|5.6|59|55|46|
|No dependents,|Female (>60% earnings)|4.4|35|65|29|
|Elderly|Male (> 60% earnings)|7.4|43|57|36|
||No labor earnings|7.3|39|61|34|
|No dependents,|Female (>60% earnings)|10.5|31|69|30|
|No elderly|Male (> 60% earnings)|28.1|31|69|38|
||No labor earnings|6.8|47|53|61|

_Note:_ ILCS = Integrated Living Conditions Survey.

# b. Findings: Impact of Taxes and Transfers across Household Types

In Armenia, direct taxes and the public contributory pension system have a large impact on improving the relative income position of either female-headed households or households where female earnings account for at least 60 percent of cumulative household income. At the same time, dual earner households contribute the highest pre-fiscal income share to income taxes while benefiting the least from direct transfers, which is an implicit disincentive for households to allocate all their labor supply to formal labor markets. Taken together, and conditional on cultural practices or community- or household-level arrangements that prioritize men as “primary” income 19
context – Latur and Nanded districts in Maharashtra. Only around 27 precent of study households had a private household toilet before the roll-out of the sanitation loan program.<sup>4</sup> Interestingly, households in this context typically identify the financial cost and affordability as the key reason for not having a toilet: 83 percent of households that do no own a toilet report that they are not able to afford one (Augsburg et al., 2015); making the sanitation loan program particularly policy relevant.

Our empirical findings indicate that, in this context, households demand the newly available loan product: two and a half years after the rollout of the new loan, 18 percent of clients have taken the loan. However, this is not a surprising finding given the lower interest rate, weak monitoring and no enforcement. Households might avail the loan to make other desired investments beyond sanitation, or for consumption purposes. They might also avail the loan to substitute for other - less favourable - credit sources. Moreover, examining data on other loans taken from the MFI, we find little evidence of substitution away from the more expensive business loan to the cheaper sanitation loan: 62 percent of clients who were eligible for a sanitation loan took a (more expensive) business loan instead. We also find no evidence that clients take more expensive business loans only after exhausting the cheaper sanitation loan: 31 percent of clients, who could have taken this combination of loans thereby reducing overall borrowing costs, chose to take a more expensive business loan only. This evidence thus indicates

> 2Unfortunately, the model does not yield insights that would allow us to separate out the effects of credit constraints or the lower interest rate based on average impacts. However, focusing on specific subgroups, e.g. those who are not credit constrained, would allow us to separately identify the relevance of these.

> 3Source: UNICEF-WHO JMP, www.washdata.org. Last accessed June 2018.

> 4Community toilets are rarely available in the study
and lowest income brackets is equal to 55 percentage points (pp)—similar to the 50-pp gap between the highest and lowest ability. Free college may therefore have ample room to raise enrollment.

**Table 1: Enrollment Rates by Income and Ability.**

|Income||Abi|lity quin|tile|||
|---|---|---|---|---|---|---|
|Bracket|1|2|3|4|5|Total|
|5+ MW|32.85|44.14|58.87|69.23|83.85|73.38|
|3-5 MW|28.71|39.75|48.41|62.99|79.24|62.03|
|2-3 MW|20.34|28.72|36.96|48.03|67.88|43.50|
|1-2 MW|13.94|18.36|23.85|33.84|54.22|28.05|
|_<_1 MW|9.05|12.67|17.20|26.56|43.93|17.67|
|Total|13.43|19.15|26.20|38.93|63.74|32.29|

_Source_ : Calculations based on SPADIES and Saber 11 for 2005 high school graduates. _Notes_ : Each cell reports percent of high school graduates from a given income bracket and ability quintile who enrolled in a bachelor’s program between 2006 and 2010. Income reported in brackets; MW = monthly minimum wage. Ability reported in quintiles of standardized Saber 11 scores; quintile 1 is the lowest.

_Fact 2: Dropout rates are high, particularly for low-ability students in year 1._

> 4To exemplify, consider a student who accumulates 16, 35, 42, 50
; ’ ,

# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS2302 Why Liberalization Alone Has Not<br>Improved Agricultural Productivity<br>in Zambia: The Role ofAsset<br>Ownership and Working Capital<br>Constraints|Klaus Deininger<br>Pedro Olinto|March 2000|M. Fernandez<br>33766|
|WPS2303 Malaria and Growth|F. Desmond McCarthy<br>Holger Wolf<br>Yi Wu|March 2000|H. Sladovich<br>37698|
|WPS2304 Disinflation and the Supply Side|Pierre-Richard Agénor<br>Lodovico Pizzati|March 2000|T. Loftus<br>36317|
|WPS2305 The !mpact of Banking Crises on<br>Money Demand and Price Stability|Maria Soledad Martinez<br>Peria|March 2000|A. Yaptemco<br>31823|
|WPS2306 International Contagion: Implications<br>for Policy|Roberto Chang<br>Giovanni Majnoni|March 2000|E. Mekhova<br>85984|
|WPS2307 Surveying Surveys and Questioning<br>Questions: Learning from World Bank<br>Experience|Francesca Racanatini<br>Scott J. Wallsten<br>Lixin Colin Xu|March 2000|P. Sintim-Aboagye<br>37644|
|WPS2308 How Small Should an Economy’s<br>Fiscal Deficit Be?<br>A Monetary<br>ProgrammingApproach|Paul Beckerman|March 2000|H. Vargas<br>38546|

.
|2025|
|Italy|4|81|2030|New Caledonia|0.3|198|2010|
|Luxembourg|0.3|<br>73|2030|New Zealard|4|123|2030|
|talta|0.3|<br>96|2030|Pacific Islands|0.4|270|2015|
|Netherlands|13|88|2030|PapuaNew Guinea|10|298|2025|
|Norway|4|94|2030|Solomon Islands|1|529|2035|
|Polard|48|130|2020|Tonga|0.2|245|2025|
|Portugal|11|104|2030|Vanuatu|1|441|2035|
|Romania|28|123|2030|WesternSamoa|0.4|240|2015|
|Spain|41|106|2030|Other Micronesia|0.1|230|2020|
|Sweden|7|87|2030|Other Polynesia|0.2|224|2010|
|Switzerland|5|79|2030|||||
|United Kingdom|56|9a|2030|World total|10837|224|2045|

Note: Populations of more than 1 million have been rounded to the nearest miltion.

(a) For a listing of countries in the "other" category, see Table 12.