rafmacalaba/data-use-mentions-extended huggingface dataset:
settlements might be much higher relative to imputed rents for individuals in rural settlements, pushing all urban dwellers above the poverty line. This, in turn, could suggest a need for a re-allocation of anti-poverty funds from urban to rural areas. Again, assessing the distributional impact of imputed rents on socio-economic characteristics of poor individuals is hard to define a priori, and it remains an empirical question.

# **3. Data and Approaches to Rent-Imputation**

This section describes first the data sources used in the analysis and second introduces the empirical strategy adopted for assessing the distributional impact of imputing rent on inequality, poverty, and shared prosperity. Two different rent-imputation models, namely, a log-linear econometric model and self-assessment method will be used in the empirical analysis.

## **3.1. Data and the Preliminary Evidence**

The analysis makes use of data from nationally representative household budget surveys that are used for the computation of official poverty figures in Albania, Bangladesh, Iraq and Peru. The countries were selected following three criteria: (i) completeness of information, (ii) broadness of regional coverage, (iii) heterogeneity in terms of development stage. The analysis requires detailed information on dwelling characteristics, tenancy status, self-assessed rental price for owners and non-market tenants, and the official methodology followed to compile the consumption aggregate used in the official distributional analysis. Further, the four countries belong to different regions and represent different stages of development, maximizing as much as possible the generalizability of the results.<sup>7</sup> The analysis for Albania is based on the 2008 and 2012 waves of the Living Standard Measurement Survey (LSMS). The two surveys are representative at urban and rural level as well as at region and prefecture level. Official poverty figures are consumption-based, and the official consumption aggregate does not include rent or imputed rent. It is important to note that we were not able to access to the methodology for determining the official poverty line. Therefore, in the following, Albania
After restricting the sample to working age individuals (between 15 and 59 years old),<sup>4</sup> since our treatment is at the provincial level, we collapsed the data into province _×_ year cells to obtain yearly out- comes of the labor markets since our treatment is at the province level.

Considering the non-panel structure of our survey data, this aggregation does not come at the cost of losing information. Therefore, in order to identify the impact of a drought shock on labor markets, we leveraged its within-province variation across years. Exploiting the details of employment status and the economic sector codes (NACE classification) for employed individuals, we focused on three labor market outcomes at the provincial level: the share of employment in the agriculture sector; the share of employment in the other economic sectors; and the unemployment rate. Other information at the provincial level includes the share of population across five age groups; the gender of the workforce; the share of educational levels across the population; the share of informal jobs; and the share of the workforce population living in urban areas.

We aligned ENE socioeconomic data with detailed weather information that we obtained from the Agri-4-Cast database. These data are provided by the Food Security Unit of the Joint Research Center (JRC.D.5) and were specifically employed for identifying the climate change impacts in the agriculture sector. The data consists of gridded meteorological observations from weather stations interpolated on a 25 _×_ 25 km grid. They are available on a daily basis from 1979 in the European Union and its neighboring countries, including Morocco. We selected variables for maximum, minimum, and mean air temperature (in Celsius degrees); mean daily wind speed at 10m ( _m/s_ ); and sum of precipitation (mm/day) to calculate the Standardized Precipitation Evapotranspiration Index (SPEI), our treatment measure. The SPEI represents a state-of-the-science indicator for measuring the impact of increased temperatures on water demand (Vicente-Serrano et al.,
surveys relating to governance are very costly to design and implement, and as a result typically cover a much smaller set of countries than polls of experts.

Sources of governance data also vary with respect to the sample of countries they cover. A number of sources cover a very large sample of developed and developing countries (EIU, DRI, HFWSJ, PRS and WDR), while others cover very narrowly-focused samples of countries (PERC for Asia, CEER and FHNT for transition economies). Some sources cover primarily developed countries but also include major developing countries (WCR, GALLUP, BERI). Since there is a strong positive association across countries between governance and per capita incomes, this difference between sources makes it difficult to compare indicators from sources which cover sets of countries with very different income levels. Similarly, there may be regional differences in govemance which hamper comparisons across sources. For example, it is not clear how to compare a governance rating based only on transition economies with one based on a broad set of countries. As discussed in our companion paper (Kaufmann, Kraay and ZoidoLobat6n (1999)), the methodology we use to construct aggregate govemance indicators takes these differences in country coverage into account. Users of the govemance database are also encouraged to keep this consideration in mind when comparing information from different sources.

In order to distinguish between indicators according to the representativeness of their country coverage, we construct a simple index which measures differences between the distribution of countries across income and regional classifications and the corresponding distribution of all countries in the world. In particular, we divide the world into a two-way classification by region and income, following the World Bank's 1998 World Development Report. For each of the sources of govemance data, we report onehalf of the sum of absolute values of the deviations between the share of countries in each of the 45 categories (five income categories x nine regions) in that source and in the world as a whole. By construction, this measure ranges from zero to one, with
that perform above the average in their countries tend to report bigger constraints than their poorly-performing compatriates. The scale of this problem is reduced when countryaverages are used; it is unlikely that a country in which the average firm is high productivity is therefore more likely to be a country in which the average firm will perceive greater investment climate obstacles.

The BEEPS and PICS datasets have a very rich set of investment indicators, and the question is how to make use of them. Two sets of investment climate indicators were used. The first set was the simple country-survey-average of each of the raw indicators reported by firms, scaled from 1 (no obstacle) to 4 (major obstacle). These variables are those in the block of questions used in the factor analysis reported in the previous chapter. The second set used sets of indicators corresponding to aspects of the investment climate, combined (e.g., a single “finance” indicator was created using the responses to questions about financial access and cost of finance). The other combined indicators were for infrastructure, taxation, regulation, the macroeconomic environment, and property rights (corruption and crime). These were scaled to be comparable to the raw data on perceived business obstacles (the minimum was 1 and the maximum was 4). In addition to augmenting the country-survey-averages regression equation (2) with the average investment climate variable, country-survey-averages of ownership variables (privatized, new private, foreign owned) and export orientation were also included. The analysis was conducted on 50-51 country-survey-averages based on about 14,000 firms from manufacturing, services and construction; the analysis separating manufacturing and services firms yielded similar results.

In these TFP estimations most of the investment climate measures in the regressions were insignificant, with one very clear and consistent exception: infrastructure constraints generate lower TFP, with an estimated coefficient of about –0.7. Countries in which firms complain a lot about infrastructure are, ceteris paribus, countries with less productive firms. The infrastructure constraints are: (i) access
on the objective facts of the weather frequently do not exist, as it would require rain gauges and thermometers at every location, as well as someone to record the relevant data on a daily (or hourly) basis. This lack of data on the objective facts of the weather is exactly why economists use remote sensing data. Because of this lack of data on the objective facts, we can only compare extraction methods, weather metrics, and remote sensing products relative to each other. For the most part, this does constrain our ability to reject or fail to reject a hypothesis. Extraction methods clearly do not impact results, there are clearly good and bad weather metrics, and some precipitation products clearly report different volumes of precipitation than others. Where we are limited by the lack of objective facts is in drawing conclusions about temperature products. Without knowing the actual temperature experienced by LSMS-ISA households, we cannot determine if the lack of correlation between temperature metrics and outcomes is either a reflection of the true relationship or evidence that all three products mismeasure temperature.

In terms of establishing a set of best practices, we recommend the following:

- Researchers need not be concerned about weather data matched to obfuscated household locations. Our results do not change substantially based on which obfuscation method we conduct the analysis on. The current spatial resolution of publicly available remote sensing weather is not fine enough for common extraction methods to result in mismeasurement of the weather that is experienced by a household. Researchers should feel comfortable matching obfuscated coordinates with weather data and need not worry that extraction methods will materially alter their results.

- Researchers must take much more care in justifying their choice of weather metric. For the LSMS-ISA data, mean daily rainfall, total seasonal rainfall, the number of rainy days, and percent of rainy days are consistent and positive predictors of outcome in a large number of models and countries. Performance of the other weather metrics are more inconsistent, frequently producing coefficients with opposite signs in similar setting. Since several weather metrics perform equally well,
|
|Kleibergen-Paap F statistic|||58.3<br>16.4|||40.4|11.1|
|# Observations|897,260|897,260|897,260<br>897,260|897,260|897,260|897,260|897,260|

_Note: Quality gap reflects the initial distance from the “quality frontier. The quality frontier is defined within a given product as the top 10% highest exporters quality. The quality gap is defined as the difference between export quality and the frontier export quality in the first year. Product complexity is measured following Levchenko (2007) using US IO tables in 1997. Robots reflects log robot stock. The instrument reflects robot diffusion in (initial) foreign export partners, in other world regions, defined using 2000 export flows. Quality refers log quality estimated via Khandelwal et al (2013). Country income status is defined by the World Bank (2000). Controls include (log) employment and (log) real value added per worker at the country-industry-year level. The first stage has been omitted for brevity. The regressions also includeexporter*10-digit product and exporter*year fixed effects. Robust standard errors clustered at the exporter-industry level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1._ 30
low employability started to get jobs. Additionally more jobs were also created in services and construction sectors – traditional hubs of shadow employment. Hence this proxy seems to be naturally ―pro-cyclical‖ – i.e. it increases as the labor demand grows.

It seems that one should have a clear idea what definition of shadow employment and model specification will be used and why. There are strong arguments to use the basic definition and specification, although it results in artificial and excessively strong interdependence between the kind of contract declared by the respondent and shadow employment probability.

One has to take into account that **those without any written contracts are the “core” of shadow employment** – they do not pay any taxes or social contributions and they are not protected by any labor regulations. It means that anyone willing to analyze shadow employment has to take them into account in estimations and hence they have to be classified into some kind of contract as available in LFS database (full/part time, fixed/open term). The kind of contract is also the only information available in the LFS directly related to the concept of shadow employment. It strictly describes the nature of employment in relation to sectors or personal characteristics of the employed.

The contract variable also has strong explanatory power for shadow employment when narrow definitions are used (so our additional reinforcement is not crucial here) and that is why the estimations when narrow definition is used produce more or less similar shadow employment results as the basic version.

Last but not least, significant share (around 17% according to the CASE_2007 survey) of those employed on fixed term basis in Poland do not have the standard employment contract but so called ―civil contracts‖ and their labor relations are not protected by labor law and labor courts. It means that although their contracts are legally binding, officially accepted and
The three channels of impact of school leadership outlined above – resource utilization, teacher behavior, and school cultures – are intrinsically connected. For example, school leaders’ ability to control the allocation of resources may extend to teachers, the largest single area of expenditure in most schools – the selection and management of whom is a key driver of school culture.

# 2.3 Context

Malawi has made impressive achievements in access to schooling since the introduction of free primary education in 1994, but its education system has failed to keep pace with increased enrolments, resulting in overcrowded classrooms and understaffed schools.<sup>4</sup> These extremely poor conditions lead to high rates of grade repetition<sup>5</sup> and dropout. In 2017/18, at the time of the baseline for this study, repetition rates nationwide averaged 32 percent in Grade 1 and 24 percent in all grades.<sup>6</sup> (Ministry of Education, Science and Technology of Malawi (MoEST), 2018) Only 59 percent of students enrolling in Grade 1 remain in school to Grade 5 (ibid.). Only one in three students who enter primary education complete all eight grades (Ravishankar et al., 2016).

Even students who remain in primary school experience poor learning outcomes. At Grade 2 level, fewer than 25 percent of students achieve minimum proficiency levels in the Early Grade Reading Assessment (EGRA) conducted by the United Stages Agency for International Development (USAID). At Grade 6, fewer than 25 percent of students achieve minimum proficiency levels in the Southern African Consortium for Monitoring Educational Quality (SACMEQ) assessment in mathematics, placing Malawi near the bottom in the region. Learning outcomes also vary widely between students, even within a single class: in 86 percent of schools, according to the baseline for this study (MLSS, 2018) the gap in knowledge scores was at least one S.D., 115 points. This is equivalent to more than two years
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" is not the same. The surveys may have been carried out in different years and with different sampling procedures.

Such general-purpose surveys typically ignore informal service options such as water vending or the provision of electricity from a private generator. Different surveys may use different definitions of some infrastructure service options.<sup>6</sup> Countries generally report summary statistics that cannot be related to the income of an individual household so that it is impossible to determine how coverage of the poor differs from coverage of other income groups. Moreover, the international agencies that compile coverage statistics for one infrastructure service (e.g., water) rarely coordinate their efforts with other agencies (or even other divisions within the same organization) interested in different infrastructure services, so it is unusual to see comparable coverage statistics reported for multiple infrastructure services.

In this paper we introduce a new data source for global coverage statistics, the World Bank's Living Standards Measurement Study (LSMS) surveys, that addresses some but not all of these limitations. These multi-topic surveys gather extensive socioeconomic and expenditure information from households, as well as limited information on a household's use of selected infrastructure services. The data used in this paper are drawn from LSMS surveys conducted in fifteen countries. The pooled sample includes more than 55,500 households in Asia, the Americas, sub-Saharan Africa, Eastern Europe, and Central Asia. The LSMS surveys enable us to examine coverage for several infrastructure services among different income groups in many different countries using householdlevel data.

The results of our analyses show that all income groups throughout the world have much higher levels of coverage for electricity than for other formal infrastructure services (in-house piped water service, sewer service, and private telephone service). In many countries most households in

> 6 For example, a respondent may be asked, "What is the household's principal water source for drinking and cooking?" Some surveys
- Guerguil, M., and Mandon, P., and Tapsoba, R., (2016). Flexible Fiscal Rules and Countercyclical Fiscal Policy _. IMF Working Paper WP/16/8_ , Washington DC: International Monetary Fund.

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- Ilzetzki, E. (2011). Rent-seeking distortions and fiscal procyclicality. _Journal of Development Economics, 96(1), 30-46_ .

- IMF. (2012). Macroeconomic Policy Frameworks for Resource-Rich Developing Countries _. IMF report_ , Washington DC: International Monetary Fund.

- IMF. (2014). Fiscal Multipliers : Size, Determinants, and Use in Macroeconomic Projections. _IMF Report_ , Washington DC: International Monetary Fund.

- Konuki, T., and M. Villafuerte. (2016). Cyclical Behavior of Fiscal Policy among Sub-Saharan Countries. _IMF report_ , Washington DC: African Department, International Monetary Fund.

- Levinson, A. (1998). Balanced Budgets and Business Cycles: Evidence from the States. _National Tax Journal, 51_ 715-732.

- Matsusaka, J. G. (2014). Disentangling the Direct and Indirect Effects of the Initiative Process. _Public Choice, 160(3-4),_ 345-366.

- Mendoza, E. G., and Oviedo, P. M. (2006). Fiscal policy and macroeconomic uncertainty in developing countries: The tale of the tormented insurer. _National Bureau of Economic Research_ , No 12586.

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8 further examined if there was a policy or epidemiological explanation for the outlying indicator value. If no such explanation could be found, we dropped the data point.<sup>5</sup> For example, we found no coding errors or definitory inconsistencies in the 2011 DHS Benin oral rehydration salts receipt rates for children with diarrhea, but nevertheless dropped the point as it was far above the rate of prior and subsequent DHS and MICS surveys (see Figure 2), and no policy or epidemiological explanation for this divergence could be found.

- <u>Figure 2: Trend chart check for rate of children under five with diarrhea receiving ORS in Benin</u>

<!-- Start of picture text --> Kept Dropped<br>60%<br>50%<br>50.7%<br>40% DHS<br>30%<br>20% 25.9% 23.5% 23.7% 25.4%<br>DHS 22.3%<br>DHS DHS MICS<br>DHS<br>10%<br>0%<br>96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17<br>Year<br><!-- End of picture text --> The trend checks also led us to exclude all insecticide-treated bed net use data points from the second round of the MICS that was carried out between 1998 and 2003, as the discrepancy in

> 5 The trend-checks underlying our exclusion decisions for the pap smear data are based on new indicator values that use a three-year reference period and women age 20-69 as our preferred age-group instead of a two-year period and women age 30-49 as in the 2018 HEFPI version. Section 3.2.2 discusses this change in detail.
The analysis is based on the consumption aggregates used for official poverty estimation. However, to allow estimation of vulnerability in the absence of assistance, we exclude selfreported food aid and cash transfer in the first consumption estimate (‘without aid’) and exclude food aid only in the second estimate (‘without food aid’).<sup>2</sup> The food aid and cash transfer information in these estimations are taken from the assistance module of the survey questionnaires. In addition, households report that the part of food consumed comes from in-kind transfers and food aid. This item-level in-kind transfer is aggregated to quantify the total amount of food transfers received by the household. By excluding the transfers, as reported in the food consumption module of the questionnaires, from the total consumption, we built an alternative index of food consumption (‘without transfers’).<sup>3</sup> Part 2 of Table 1 presents the measures of weather shocks. To select the appropriate drought and rainfall indexes, we mapped several indicators over multiple years. Existing empirical evidence and expert knowledge of which districts were hit most by dramatic weather events and the years extreme weather events guided the selection of indicators. The measure of drought used in this study is based on CRU’s Global SC-PDSI during the growing season. It is calculated from temperature, precipitation, and locally available soil water content (Barichivich and Jones, 2018; van der Schrier et al., 2013). We averaged this index (for each month) at the TA level to merge it with the IHS. The Palmer index varies from −10 to 10 with values lower than −2 indicating droughts. For TAs with Palmer index above −2, that is, those that did not experience drought, a value of zero is assigned. For those with index below −2, the absolute value of the index is used such that higher positive values correspond with severe droughts. This definition makes the interpretation of the drought coefficient easier.