rafmacalaba/data-use-mentions-extended huggingface dataset:
upper)<sup>3</sup> . We find that across the transition region, the poorest third of the population in each country is more likely to report that their household was adversely impacted by the crisis (see Table 4). Similarly, household asset ownership (ownership of a car, a secondary residence or a bank account) is negatively correlated with crisis impact. The greater subjective impact of the crisis on poorer households is consistent with the fact that they are much more likely than richer households to have experienced job losses.<sup>4</sup> The subjective impact on the poorest households is greatest in Turkey, the western CIS (BelarusMoldova-Ukraine) sub-region, the new EU member states and the south Caucasus. However, in Russia, Central Asia and south-eastern Europe, the LiTS II data do not suggest a differential subjective impact for the poorest third of households relative to richer households. Lastly, female-headed households<sup>5</sup> and the elderly (or pensioners) do not report a significantly greater crisis impact than other households. These findings based on household perceptions corroborate some of the preliminary or partial information from objective measures but there are also some significant differences. Indeed, consumption data (from Household Budget Surveys) suggest that the crisis affected mostly middle-and upper-income groups (Sulla, 2011). This is consistent with the LiTS II finding that upper- or middle-class households report the largest drops in their social position (relative income). Nevertheless, poorer LiTS households (as measured by their current consumption) also report more being (subjectively) affected by the crisis than richer households.

There could be many reasons why subjective assessments may differ from objective measures. For example, the trauma associated with job-loss can go beyond the associated drop in household income, there may be increased insecurity about the future, and so on. The subjective impact of the crisis may therefore depend on the coping strategies adopted, the intrinsic value of employment, and the extent to which households value
Figure 5: Percentage of Labor Force in Each Country with a Low Quality Job, _k ≤_ 0 _._ 2 <!-- Start of picture text --> Arg Bol Bra Chl Col CriDom EcuGtm Hnd Mex Nic Pan Per Pry Slv Ury<br>.6<br>.4<br>Percentage<br>.2<br>0<br><!-- End of picture text --> _Notes_ : Authors’ calculations using SEDLAC data. Sample size and years covered differs by country as described in Table 1.

receiving successes in all dimensions of job quality. This implies that the distribution of job quality in Argentina is split between workers with very high levels of job quality and those with moderately low levels. A similar observation holds for Mexico as well.

To detect the presence of bimodality, Figure 5 shows the percentage of workers in each country with a “low quality job”. A low quality job is defined here as having less than 20% of the quality dimensions satisfied. If countries have a large number of low quality jobs and a large number of good jobs as shown in Figures 3 and 4, then there is a large disparity of job quality throughout the country. Low quality jobs are distributed as expected, but there is evidence of bimodality, and thus inequality, in job quality in Argentina, Chile, Costa Rica, and Mexico. Bolivia, Guatemala, 20
WPS3460

**Schooling and Labor Market Impacts of a Natural Policy Experiment**

Harry Anthony Patrinos<sup>*</sup> The World Bank <u>hpatrinos@worldbank.org</u> Chris Sakellariou<sup>∗</sup> School of Humanities and Social Sciences Nanyang Technological University, Singapore <u>acsake@ntu.edu.sg</u>

**Abstract** : We use a nationally representative household survey to estimate returns to schooling in Venezuela from instrumental variables based on a supply-side intervention in the education market. These estimates apply to a subgroup of liquidity-constrained individuals, in the spirit of the local average treatment effect (LATE) literature. Returns to schooling estimates that apply to a subgroup of individuals affected by the policy intervention may be more interesting from a policy perspective than the return to the “average” individual. We use an instrument based on the 1980 education reform (the Organic Law of Education), which provided for nine years of compulsory basic education. Alternative estimates using father’s education as an instrument are also obtained in an attempt to derive high and low estimates of returns to schooling in Venezuela. The estimates are consistent with recent findings suggesting that the effect of education, at least for certain subgroups affected by policy intervention, is as large as or larger than what is suggested by ordinary least squares estimates.

JEL Classification: J24, J31 Key words: Endogenous education, education reform, treatment effects, Venezuela

# World Bank Policy Research Working Paper XXXX, Month 2004

_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 view of the World Bank,
|(0.009)|(0.011)|
|(2) both same sex|0.502|0.487|0.387|0.595|
|||(0.008)|(0.011)|(0.011)|
|Diference (2) - (1)|0.005|0.140|0.131|0.150|
||(0.011)|(0.011)***|(0.014)***|(0.016)***|
|Observations|7480|7480|3874|3606|

_Notes:_ The data used are the 2002 LSMS, the 2005 LSMS, the 2008-09 DHS and the 2012 LSMS. * Indicates statistical significance at 10%. ** Indicates statistical significance at 5%. *** Indicates statistical significance at 1%. Standard errors are in parenthesis.

37
2003-04 and 2010-11 (for Nepal) and the LFS (for Pakistan) count activities such as fetching water and collecting firewood and dung as employment, whereas sources in other countries do not. To ensure comparability, we excluded such activities in all countries.

The structure and framing of employment questions may vary even within the same primary source. In Afghanistan and Pakistan, for example, employment modules are different for males and females. In Bangladesh, the HIES asks employment questions in two different modules. A first set of questions is asked in the roster module while a second set appears later (module 5: A & B). This breakdown sometimes results in inconsistent responses.

A second difference in the definition of employment across data sources concerns the identification of the main activity of the respondent. For example, in the Bangladesh HIES each respondent lists several activities he or she was involved in, but only the activity absorbing the most time is registered. In India, on the other hand, the NSS makes a distinction between ‘principal’ and ‘subsidiary’ activities based on the duration of the work. We include only the ‘principal’ status in our dataset.

The recall period is a third important difference between data sources. For example, in Afghanistan the 2007-08 NRVA uses a recall period of one month when asking respondents whether they are employed, while the 2011-12 NRVA has a recall period of one week. The India NSS includes a weekly status, but it is constructed based on questions about time use over half-days. The yearly status, on the other hand, is identified based on a specific question to the respondent. The Pakistan LFS uses a recall period of one week, but the period is one month in the Pakistan HIES. In Sri Lanka, some of the surveys do not specify a recall period at all when inquiring about employment. The majority of Sri Lanka’s HIESs have information about the usual employment status, while the LFS reports the weekly employment status.

Fourth, the dates when information
Policy Research Working Paper 9335

# **Abstract**

This study developed a land cover and land use mapping framework specifically designed for agricultural systems in the Sudan-Sahel region. The mapping approach extracts information from inter- and intra-annual vegetation dynamics from dense stacks of Landsat 8 images. The framework was applied to create a 30-meter spatial resolution land use map focusing on the 2015 agricultural landscapes in northern Nigeria. The map provides up-todate information with a high level of spatial and thematic detail, resulting in a more precise characterization of agriculture in the region. The map reveals that agriculture is the main land use in the region. Arable land represents on average 52.5 percent of the area, which is higher than the reported national average for Nigeria (38.4 percent). Irrigated agriculture covers nearly 2.2 percent of the total area, reaching nearly 20 percent of the cultivated land when traditional floodplain agriculture systems are included, which is above the reported national average (0.63 percent). There is significant variability in land use in the region. This study demonstrates the feasibility of multitemporal medium-resolution remote sensing data to provide detailed, up-to-date information on agricultural systems in the arid and sub-arid landscapes of the Sahel region.

This paper is a product of the Poverty and Equity Global Practice. 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 vmolini@worldbank.org or mazad@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
and camp-like settings. Second, we examine how attitude formation is shaped by networks, suggesting that the ‘contact effect’ may also be shaped by dynamics at the intra-group level. It implies that one of the limiting factors underlying the positive impact of contact may be the way it is mediated through neighbourhood and household level discussions. Although it is beyond the scope of the paper to examine this intra-group attitude formation process, it is something that is touched upon within the literature on the economics of information’s discussion of, for example, ‘echo chamber effects’, which describe non-Bayesian behaviours at an intra-group level (B ́enabou 2015; B ́enabou and Tirole 2016; Levy and Razin 2019).

# **4 Research Design**

Our analysis relies on quantitative data (Section 4.1) and qualitative data (Section 4.2) collected in refugee camps, settlements, and cities in Kenya, Uganda, and Ethiopia. We use OLS and IV regressions (Section 4.3) to assess the extent to which interactions between refugees and hosts affect their perceptions of the other community.

## **4.1 Quantitative Data**

Our analysis uses first-hand data collected in Kenya, Uganda, and Ethiopia between 2016 – and 2018 (n= 16,608). Our survey covered the three capital cities Nairobi, Kampala, and – Addis Ababa and three groups of camps or settlements, the Kakuma camps in Kenya, the Nakivale settlement in Uganda, and the Dollo Ado camps in Ethiopia. The first data collection took place in Kakuma in November-December 2016, followed by Nairobi in May 2017, and both Ugandan research sites in April 2018. Finally, data collections in Addis Ababa and Dollo Ado were undertaken between September and December 2018.

In each site, we surveyed representative samples of the numerically most significant refugee populations and the host nationals in the community living nearby. For camp-like contexts, this means nationals of the three countries residing in towns and villages in the immediate vicinity of
and sub-national statistics to 5 arcminute pixels based on satellite-derived information of constituents of AgGDP, including forestry, hunting, and fishing, as well as cultivation of crops and livestock production.<sup>1</sup> We make two main contributions. First, we construct a global dataset of gridded AgGDP. This entails a massive effort of data collection and integration. We extend and apply the cross-entropy framework developed in the Spatial Production Allocation Model (SPAM) for crops that pioneered the use of cross-entropy optimization in spatial allocation (You and Wood, 2003; You, Wood, et al., 2014; You, Wood-Sichra, et al., 2018; Yu et al., 2020). We construct and integrate global datasets of the components of agricultural GDP as priors and then reconcile the values with the regional account statistics using cross-entropy optimization. Second, we contribute to efforts assessing the exposure of economic activity to natural hazards with a focus on agricultural GDP. Significant progress has been made to measure physical assets such as built-up area and estimate hazards to quantify its exposure to natural disasters. However, the spatial distribution of agricultural GDP is less known. So, we apply these data to inform efforts quantifying the population and agricultural GDP at risk to drought and water scarcity.

The rest of this paper is structured as follows. The next section provides a detailed description of the methodology and data. Then, we present the model results and data. Then, we discuss the results along with validation followed by usage notes from a fitness-foruse perspective. Finally, we provide concluding remarks.

# **2 Methodology and data**

Following the composite structure of agricultural GDP, we disaggregate the national and sub-national statistics into a global grid through a cross-entropy allocation model. Given the availability of data and the global scope, our efforts varied on adjusting official statistics and creating priors for different components. Below we discuss the construction of each component, AgGDP statistics and the allocation model followed by the global natural hazards data. Given the
There is likely to be heterogeneity in impacts within one area at a given time, which can also fuel debate. Consider the different views of Akram-Lodhi (2004, 2005) and the Center for Rural Progress (2004). Both draw (in part) on field work in the Mekong Delta, and at about the same time; yet they come to very different conclusions, one favoring PILE and the other not. This may partly reflect horizontal inequalities in economic and social change, whereby similar people _ex ante_ fare differently; two researchers can then come back from field work at the same time in the same area with very different stories, depending on who they talk to. This speaks to the need for representative survey data when attempting to form generalizations.

Later we will see what light survey data can cast on these issues. However, the debate will not be resolved by data alone. There are also differences in the value judgments made. Some observers see rising land inequality as a bad thing _per se_ , even if it comes with falling poverty. Here we will assume that, while inequalities in various dimensions may be instrumentally important to absolute levels of living, the latter are the overriding consideration.

Differences in the attribution of empirical observations to causes can also play a role. Suppose that a household suffers a serious health crisis, and has to sell its land. The household becomes both poor and landless. This would not have happened if there had been a ban on selling land. But the existence of a land market did not cause the poverty; indeed, the absence of a market could entail even greater poverty, by effectively de-valuing the household’s main asset.

# **3. Land markets, occupational choice and welfare**

The issues in this debate can be captured in a transparent way using a relatively simple model of how the occupational choices of poor farm-households are affected by a policy reform that removes restrictions on land markets. The model essentially pits the gains from expanded choice and specialization against
25 high-income and 23 middle-income countries. Since some countries did not start offering factoring services (or collecting aggregate measures) until after 1994, we use an unbalanced panel of 479 observations. Complete definitions of all variables are shown in Table 1 and summary statistics, by country, are shown in Tables 2 and 3. We calculate our dependent variable as the ratio of total factoring turnover to GDP. The average factoring ratio for the countries in our sample is 1.81% We test the hypothesis that there is a relation between factoring and local macroeconomic and business environment variables. As discussed in the previous section, ordinary factoring requires comprehensive credit information on all buyers and a legal environment that supports the sale of accounts receivables and the enforcement of factoring contracts.<sup>5</sup> We also expect that firms have larger sales and a greater volume of receivables to factor when the economy is healthy and growing, so that factoring should be positively related to real GDP and GDP growth. The relation between factoring and measures of bank credit is less clear: On the one hand, factoring services may be provided as a complement to banking services in countries with overall financial development, while on the other hand, factoring may substitute for bank financing in countries with less developed banking sectors.

To test these hypotheses, we include as our explanatory variables measures of macroeconomic development and the strength of the business environment. First, we include the 1-year lagged value of logged real GDP per capita (LGDPPCt-1) as a broad measure of development. Next, we include the 1-year lagged value of the 1-year growth rate of GDP as an indicator of economic growth (GDPG1t-1). Finally, we include the 1-

> 5 Cross-country data is not available on the relative tax treatment of factoring or whether the law explicitly addresses factoring transactions.

12
the socio-demographic attributes of the head of household (age, education, marital status) are used.

LSMS household surveys also include information on vehicle ownership. Questions on vehicle ownership are included in the module of durable goods or household assets. The set of questions typically ask respondents whether their household own a list of vehicles (for example, car, motorcycle, minibus, bicycle, boat, animal-drawn cart), the number of vehicles they own, date of acquisition, purchase price, and estimated current resale price. However, the list of vehicles and the sophistication of questions vary by country, which means that some questionnaires do not collect sufficient information to estimate the annual use value of vehicles, calculated based on the purchase and resell prices, and the date of acquisition.<sup>4</sup> Table 2: LSMS surveys countries

|**Collection**|**Country**|**Region**|**Incomegroup**|**Survey name**|**Survey year**|
|---|---|---|---|---|---|
||Burkina Faso|AFR|Low income|Continuous Multisectoral Survey|2014|
||Ethiopia|AFR|Low income|Socioeconomic Survey|2015/16|
||Iraq|MENA|Upper middle income|Household Socioeconomic Survey|2012|
||Malawi|AFR|Low income|Integrated Agricultural Survey|2016/17|
|**LSMS S**|Mali|AFR|Low income|Living Standards Survey|2014/15|
|**urveys**|Nepal|SAR|Low income|National Survey on Household Living Conditions and Agriculture|2010/11|
||Niger|AFR|Low income|General Household Survey|2014/15|
||Nigeria|AFR|Low middle income|National Panel Survey|2015/16|
||Tanzania|AFR|Low income|
_Figure 6: Simulation of income shock among rural households._ <!-- Start of picture text --> 100<br>90<br>80<br>76<br>70<br>65<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 0.5 1 1.5 2 2.5 3 3.5 4<br>Daily core consumption expenditure per capita (US$)<br>Poverty change Core consumption (2017)<br>Income shock<br>% of population<br><!-- End of picture text -->

# Source: Authors’ calculations.

The simulation results emphasize that a sustainable poverty reduction strategy should involve making rural households more resilient to climatic income shocks. To guide such efforts, it is instructive to analyze the characteristics of the most affected rural households. To do this, we focus on which households self‐ reported having been affected by the 2016/17 drought, regressing it on household characteristics while controlling for location, income, and households’ exogenous level of drought exposure as measured with NDVI. First, households relying on agricultural income are 20 percent more likely to report being affected by the drought. In contrast, households relying on salaried labor are significantly less likely to report being affected by the drought (Figure 7). The fact that households relying on agricultural income are mainly in rural areas is likely part of the reason why no drought effect was found in urban areas. The particular vulnerability to drought shocks of agricultural households is also well‐documented in the literature (e.g. Hill and Mejia‐Mantilla, 2017). This set of findings suggests that agricultural households may benefit from insurance products, such as agricultural index insurance (see Berhane et al., 2012; Dercon et al., 2014). Further, measures facilitating the diversification of income sources
Figure 3: Investment in digital, informality, and uncertainty <!-- Start of picture text --> (a) Investment in digital vs. informality (b) Investment in digital vs. uncertainty by type of informality<br>.4 Round 1 .35 Necessity<br>Round 2 De Soto<br>Round 3 Parasite<br>.3 Fully formal<br>.3<br>.2<br>.25<br>.1<br>.2<br>0<br>.15<br>-.1<br>0 .2 .4 .6 .8 -20 0 20 40 60<br>Informality (PCA score) Uncertainty<br>Probability of investing in digital technologies Probability of investment in digital technologies<br><!-- End of picture text -->

**Notes:** Controls for size, sector, age, gender of the CEO, country, panel firms, and initial digital readiness. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

Figure 4: Shapley decomposition investment digital <!-- Start of picture text --> 45.2<br>28.9<br>24.5 24.9<br>22<br>20.1<br>15.7<br>8.5 8.5<br>1.6<br>Round 2 Round 3<br>Informality External attributes<br>Severity of the lockdown (mobility) Severity of demand shock<br>Constraints<br>60<br>40<br>20<br>Pseudo R2 explained by group<br>Relative Shapley Value (%) - Probability of investing in digital<br>0<br><!-- End of picture text --> Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS
- In Tanzania, data were assembled from the National Panel Survey (NPS) across five periods: 2008/2009, 2010/2011, 2012/2013, 2014/2015 and 2019/2021. Split off households were tracked in Tanzania (National Bureau of Statistics, 2021b). A “refresh” sample was added to the panel in 2014/2015 and interviewed again in 2020/2021 as part of wave 5. Also in 2014/2015, a representative sub-sample of the panel which started in 2008 was selected to form the “extended” sample, which was re-interviewed in 2019/2020 as part of a survey with sex disaggregated data (NPS-SDD). For practical purposes, we denote the NPS-SDD, along with the 2020/2021 survey of households as part of the refresh sample, as “wave 5”.

- In Uganda, data were assembled from the Uganda National Panel Survey (UNPS) across seven periods: 2009/2010, 2010/2011, 2011/2012, 2013/2014, 2015/2016, 2018/2019, 2019/2020. In Wave 4 (2013/2014), one third of the sample was refreshed. Split off households were tracked for a subset of the total sample, consisting of 20% of the baseline households. At the time of writing, wave 6 was not available to the public and is therefore absent from the HP.

More detailed survey-specific information, including logistical and operational details and step-by-step descriptions of the calculations of sampling weights for each survey can be found in the Basic Information Documents for each survey which are publicly available on the World Bank’s Microdata Library. We provide links to these documents in Table 1.

6