rafmacalaba/data-use-mentions-extended huggingface dataset:
younger and female workers are more likely to lose their job. However, exposure to unemployment is not concentrated in particular income groups, as these individuals are found across the whole welfare distribution (see Figure 5).

_Figure 5: Unemployed workers by decile of the welfare distribution_ <!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Household income decile<br>population<br>Newly unemployed as a share of total<br><!-- End of picture text --> This result suggests that the ultimate distributive impact of the unemployment increase would depend heavily on the social protection programs available to those who lost their job and, of course, to the whole population. However, it is unlikely that these programs and, more generally, public transfers and expenditure will not suffer from the shrinking oil royalties following the drop in oil price. The oil price shock, indeed, puts government expenditure under pressure and its reduction is likely to have affected differently individuals located in different parts of the welfare distribution.

Taking advantage of the availability of data on public transfers in a different survey, the RLMS (Russian Longitudinal Monitoring Survey), our second simulation estimates the effect of a 10 percent cut in public transfers. The impact on poverty and shared prosperity depends on the incidence of the transfers along the welfare distribution: if poor households depend relatively more than the richer ones on public transfers, the final effect of the oil shock could be reversed once the cut of transfers is taken into account.

Most of the beneficiaries of public transfers<sup>21</sup> and pensions are indeed located in the first decile of the consumption distribution (see Figure 6) while the share is much smaller for households located in the top 30 percent of the distribution.

> 21
> Source: authors’ own computation with data from CEDLAS and The World Bank (2015) In the remainder of this analysis, we develop the cross-sectional breakdown from Figure 8 into a more detailed, longitudinal analysis to observe how the composition of ninis across these dimensions has changed over the past two decades. Here again, we analyze trends for the 15 to 18 and 19 to 24 age groups separately, to reflect the possibility that school dropout and labor market participation decisions differ in nature across these subgroups. From a policy perspective, being a nini between the ages of 15 and 18 is particularly worrisome, because at this stage in the life cycle many societies have affirmed that formal education is the most socially productive activity in which youth can be engaged. Youth under 18 years of age are still of school age and are still developing physically, mentally, and emotionally. During these critical years, being in school in a protected and constructive environment contributes to the development of cognitive and socio-emotional skills, and it can also be a mechanism for becoming integrated into the community, acquiring social values, and developing trust in institutions and the rule of law. In contrast, the story for 19 to 24 year-old ninis may be more one of youth unemployment; nevertheless, we present results for both subgroups both to understand the heterogeneity of ninis as a group, and for comparison purposes.

15
# **6.2 Education**

A perhaps surprising result is that even in the presence of local violence shocks, many children still manage to go to school. A few previous studies have documented similar no-effect relationships between local conflict intensity and education outcomes, such as the study by Pivovarova and Swee (2015) using panel data from Nepal. Yet, our finding contrasts with the majority of earlier studies that documented negative impacts of violence exposure on educational outcomes, including in North-east Nigeria (Bertoni et al., 2018). These differences might have to do with the fact that we study short-term processes and outcomes. That is, for education we analyze recent violence shocks and current school attendance, which differs from many previous studies. Our finding that the impacts on education outcomes are gender-specific are in line with robust findings in other conflict-affected settings (e.g. Shemyakina, 2011).

# **6.3 Policy implications**

More broadly, our findings add important nuances to the understanding of the challenges that situations of forced displacement and armed conflict entail. It has become increasingly clear that many behaviors and outcomes in such settings will not have mono-causal or mono-level explanations. We use an intersectionality lens to contribute detailed insights into the interplay of forced displacement, gender and violence in jointly shaping crisis responses at the household, adult and child levels. Our outcomes of interest – adverse coping behaviors in food security, school attendance and economic activity – are key targets of support interventions in crisis and displacement settings.

Therefore, our findings on the compounding challenges stemming from forced displacement, gender and violence challenge some of the ways in which many policies and programs are designed. For example, policies and interventions to support women in conflict settings have often sought to address gender inequalities or the needs of the displaced in isolation. Yet, our findings emphasize the need for and potential of concerted policy approaches that account for the intersectionality of gender and displacement in these settings. The findings confirm that effective support for women should target
# **Annex A: Summary description of the Envisage model and the model dimensions**

The description of the Envisage Model follows the circular flow of an economy paradigm. Firms purchase input factors (for example labor and capital) to produce goods and services. Households receive factor income and in turn demand goods and services produced by firms. And, equality of supply and demand determine equilibrium prices for factors, goods and services. The model is solved as a sequence of comparative static equilibria where the factors of production are exogenous for each time period and linked between time periods with accumulation expressions.

Production is implemented as a series of nested constant-elasticity-of-substitution (CES) functions the aim of which is to capture the substitutability across all inputs. Three production archetypes are implemented. The first is for crops that reflects intensification of inputs versus land extensification. The second is for livestock that reflects range-fed versus ranch-fed production. The final, also referred to as the default, revolves largely around capital/labor substitutability. Some production activities highlight specific inputs (for example agricultural chemicals in crops and feed in livestock) and all activities include energy and its components as part of the cost minimization paradigm. Production is also identified by vintage—divided into _Old_ and _New_ — with typically lower substitution possibilities associated with _Old_ capital.

Each production activity is allowed to produce more than one commodity—for example the ethanol sector can produce ethanol and distiller’s dried grains with solubles (DDGS). And commodities can be formed by the output of one or more activities (for example electricity). ENVISAGE therefore uses a different classification of activities and commodities.<sup>18</sup> One of the features of the model is that it integrates the new GTAP power data base that disaggregates GTAP’s electricity sector (‘ely’) into 11 different power sources plus electricity transmission and distribution. Though the database has both the supply and demand side for all 11 power sources, the aggregation facility permits the aggregation of electricity demand into a single
2015 data is relatively small. This suggests that the spatial variation of the 2017 data is still relevant to analyze the role of long-term pollution exposure in the current situation. The temporal lag in the pollution data also ensures that there is no endogeneity due to feedback between case incidence and changes in pollution levels that follow lock-down policies.

To test whether the main findings of the analysis generalize to other pollution data sets, a second analysis presented in the appendix uses the coarser grids from the global PM2 _._ 5 data set of van Donkelaar et al. (2016). The main conclusions of the analysis do not change when this alternative pollution data set is used, and since this data is mainly satellite-derived, it may be used in other countries where detailed PM2 _._ 5 measurements are not easily available. Figure 6 visualizes the spatial distribution of the main PM2 _._ 5 and PM10 statistics. Table 3 summarizes the full set of covariates used in the analysis.

# **4. Results**

The analysis is organized into two main investigations and a set of robustness analyses. First, section 4.1 analyzes the confirmed cases per 100,000 inhabitants using linear models that account for possible spatial autocorrelation and residual dependence. Section 4.2 analyzes the data nonlinearly, allowing parameter estimates to vary across locations and levels in the data. Additional results are included in the appendix, section 6.2. In particular, the robustness of the results is diagnosed by using alternative measures of incidence, a different source of pollution data, and alternative distributional assumptions.

> 10. https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische-data/wijk-en-buurtkaart-2019

> 11. https://www.rivm.nl/media/smap/langdurigeziekte.html

> 12. https://www.atlasleefomgeving.nl/kaarten

5
World Bank, Washington, USA.

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- Verwimp, P., Bundervoet, T. (2009) Civil War and the Welfare of Extended Households: Evidence from Longitudinal Data from Burundi. HiCN Working Papers, no. 70.

- Willems, R., Kleingeld, J., Leeuwen, M. (2010) Connecting Community Security and DDR : Experiences from Burundi. Peace, Security and Development Network, The Hague, The Netherlands.

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**Figure III-1: Medium-term Credit Cycle and Credit Cycle Peaks**

<!-- Start of picture text --> SAU: Medium Term Credit Cycle and Credit Cycle Peaks EGY: Medium Term Credit Cycle and Credit Cycle Peaks<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br>Medium-term Credit Cycle Peak Medium-term Credit Cycle Peak<br>USA: Medium Term Credit Cycle and Credit Cycle Peaks<br>1967 1977 1987 1997 2007 2017<br>year<br>Medium-term Credit Cycle Peak<br>15 1 20 1<br>10 .8 .8<br>10<br>5 .6 .6<br>0<br>percent0 .4 percent .4<br>local_peak_SAU local_peak_EGY<br>-5 -10<br>.2 .2<br>-10<br>0 -20 0<br>1<br>5<br>.8<br>.6<br>0<br>percent .4<br>local_peak_USA<br>.2<br>-5<br>0<br><!-- End of picture text --> As the second step, we investigate the relation between the peaks in credit cycles and recessions, measured by the GDP growth of the countries in our sample. According to the literature for Western countries and Japan, every peak in a credit cycle is followed by a period of recession with a little lag.

To check the relation between credit cycles and the GDP growth of the countries, Figure III-2 demonstrates the credit cycle peaks with the growth of GDP. In the United States, after every peak in credit cycle a sharp drop in GDP growth follows even if the GDP growth is quite high before and during the peak. Nonetheless, in Saudi Arabia and Egypt the drop in GDP growth after the credit cycle peaks is not as evident and it is not clear
Policy Research Working Paper 5501

# **Abstract**

Consumption expenditure has long been the preferred measure of household living standards. However, accurate measurement is a challenge and household expenditure surveys vary widely across many dimensions, including the level of reporting, the length of the reference period, and the degree of commodity detail. These variations occur both across countries and also over time within countries. There is little current understanding of the implications of such changes for spatially and temporally consistent measurement of household consumption and poverty. A field experiment in Tanzania tests eight alternative methods to measure household consumption on a sample of 4,000 households. There are significant differences between consumption reported by the benchmark personal diary and other diary and recall formats. Under-reporting is particularly relevant in illiterate households and for urban respondents completing household diaries; recall modules measure lower consumption than a personal diary, with larger gaps among poorer households and households with more adult members. Variations in reporting accuracy by household characteristics are also discussed and differences in measured poverty as a result of survey design are explored. The study concludes with recommendations for methods of survey based consumption measurement in low-income countries.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at kbeegle@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank
which we could only apply the short survey.

We stacked both rounds of VenRePS and constructed a baseline using the date of birth of household members prior to the opening of RAMV (April 5, 2018). By that point in time, no households were beneficiaries of the PEP-RAMV program. For each of the three waves (baseline, VenRePS, and VenRePS follow-up), we observe the age of the head of household’s children who were born in Colombia.<sup>11</sup> We excluded from the analysis children who were conceived before the PEP-RAMV announcement (August 2, 2018) since the program could not have affected the decision to have these children.

> 11As a consequence of a decree issued in 2019, all children born in Colombia to Venezuelan parents are Colombian.

35
# **4.2. Land fragmentation**

Agricultural holdings do not normally comprise a single compact block but are made up of a number of parcels scattered across the village. The fragmentation<sup>32</sup> of operational holdings into multiple plots is commonly perceived to be a serious constraint on agricultural productivity. High direct and opportunity costs in cultivation are frequently ascribed to fragmentation, including: the time and energy expended in moving labor, draft animals, seed, manure and irrigation water from one plot to another, and bringing harvested crops to a common point; supervision of labor; increased expenses of irrigation and drainage; difficulty of access to scattered plots; and loss of land in boundaries (Mearns 1998).

State governments have attempted to control fragmentation through legislative means and by encouraging land consolidation, or making it compulsory. This section examines the extent of fragmentation in Orissa, legal provisions to control it, and progress achieved and problems encountered in the administrative process of land consolidation.

## _4.2.1 Extent offragmentation_

There is widespread consensus that the rate of fragmnentation of land holdings in Orissa is very high, though the evidence is patchy. This is because there is no systematic compilation of data within the state on the number of parcels per operational holding. These data is collected at the time of consolidation operations, and may even be sent to the Director, Consolidation at Cuttack. But they are rarely aggregated so as to form a comprehensive view of the extent of fragmentation, regional variation, or of the relative success of consolidation. Thus, much of this discussion is based on village studies, National Sample Survey (NSS) data, and informed estimates.

The trend in fragmentation over time and across land sizes is shown in Table 9. In 1961-62 there were an average of 6.4 fragments (or parcels) per operational holding across all size classes. By 1981-82, this had fallen to 5 parcels per holding. The average area per parcel has also declined during this
2

# **I. Introduction**

Macroeconomic analysis suggests that regional budgets in Russia have been becoming a relatively more important source of those budget subsidies and transfers that remain in the economy. While subsidies funded by the federal government were cut dramatically over 1992-95, those funded by regional authorities demonstrated the opposite trend: they increased during 1992-94 and even after a significant drop in 1995, when measured as a percent of GDP, they were still higher than in 1992. The total volume of subsidies' funded by regional budgets in 1995 as estimated amounted to 5.2% of GDP, while the federal government spent less than 2% of GDP for these purposes.

At the same time, recent regional developments in Russia demonstrate the large and growing variation in models of economic policy conducted by regional governments. (Lavrov, 1996b; Mau and Stupin, 1997; Polishchuk, 1996; TACIS, 1996; Vereshagin, 1996). In a situation where the federal government has a limited leverage on regional decision-making, regional policies are heavily influenced by numerous local factors, which _de facto_ lead to regional experimentation with quite different (sometimes contradictory) policies and approaches to the reform process. The incidence of regional subsidies is an important indicator in such an environment because it could be considered as an informative measure of the policy priorities of particular regional governments as well as of the speed of the regional reform process in general (Balcerowicz and Gelb, 1994; Lavrov, 1996b).

The primary idea of this paper was to examine inter-regional variation in subsidy allocation and to explore the basic determinants of budget subsidies in the 87 regions of the Russian Federation for which data are available from 1992-95.2 Central to the analysis has been the attempt to provide some answers to the following three questions: (i) How large is the variation across regions in the incidence of subsidies and what are the recent trends in such variation?

(ii) What are the relative influences of supply and demand factors in determining the current levels of subsidization in Russian
Economic Inequality_ , _14_ , 141–172.

- Florczyk, A. J., Corbane, C., Ehrlich, D., Freire, S., Kemper, T., Maffenini, L., Melchiorri, M., Pesaresi, M., Politis, P., Schiavina, M., Sabo, F., & Zanchetta, L. (2019). GHSL data package 2019, JRC Technical Report.

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- Gibson, J., Jiang, Y., & Susantono, B. (2023). Revisiting the role of secondary towns: How different types of urban growth relate to poverty in Indonesia. _World Development_ , _169_ .

- Glaeser, E. L., & Gottlieb, J. D. (2009). The wealth of cities: Agglomeration economies and spatial equilibrium in the United States. _Journal of Economic Literature_ , _47_ (4), 983–1028.

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- Heinrigs, P
the firm for participating, compliance may be lower. For example, in an experiment to evaluate the effect of financial literacy training on female entrepreneurs in Uganda, only half of the treatment group invited to participate in the training actually attended (McKenzie and Weber, 2009). Other recent business training experiments have found higher attendance rates when given to members of microfinance groups (e.g. Berge et al. (2010) report 83% of the women in their business training experiment in Tanzania attended often enough to get the completion certificate), but 100 percent compliance is unlikely.

Table 5 reports the power of an experiment with 300 treatment and 300 control firms to detect the types of treatment effects often targeted as results of projects like those in Tables 3 and 4.<sup>6</sup> It also reports the size of the treatment group one would need in order to achieve 90 percent power, assuming an equal-sized control group. The results in the first column show that if a oneoff follow-up survey is conducted of treatment and control firms, the sample sizes needed to detect program effects of interest are larger than the number of firms participating in typical SME programs, and indeed larger than the total number of manufacturing firms with 10 or more employees that exist in some African countries. For example, with treatment and control groups of 300 firms each, the power is only 12.9 percent for being able to detect a 20 percent increase in sales, 41.7 percent for detecting a 10 percent increase in employment, and 36.8 percent to detect a 5 percentage point increase in the proportion of firms exporting (from an assumed baseline rate of 12% of firms exporting).

The next three columns consider the improvement in power from also using baseline data, under a variety of assumptions about the autocorrelation in firm outcomes (ρ). Firm data exhibit considerable variability, much of it seemingly genuine (Fafchamps et al. 2010a), and available data suggest that the autocorrelation of profits or