rafmacalaba/data-use-mentions-extended huggingface dataset:
by the Earth Observation Group at the Colorado School of Mines.<sup>5</sup> To measure pollution, we use Nitrogen Dioxide (NO2) concentrations measured by Sentinel 5-P satellites.<sup>6</sup> We expect that NO2 levels increase in response to human activity, namely burning fossil fuels and biomass, which may have fallen during the crisis. EVI was taken from the Landsat 8 Collection 1 Tier 1 composite.<sup>7</sup> The Google search index at subnational levels reflects the relative popularity of any given search term. The total search volume for each term is divided by total Google search volume for that week and subnational region. The search index for each term is rescaled such that the week-subnational region observation with the highest relative search score, for all weeks in that country, is assigned a value of 100 with all other weeks expressed relative to this peak. The baseline (weeks with a value of 100) itself is a mechanism for rescaling search term data such that values are comparable across regions and over time.<sup>8</sup> We aggregate data into monthly averages, taking the average of the weekly data points for each subnational district.

Google restricts the quantity of data that can be downloaded in one batch. Therefore, the datasets are extracted in multiple rounds to cover all subnational districts for all countries with HFPS data. This process requires indexing all downloads against a common administrative district which serves as a reference point. The reference district is included in all rounds of downloads such that the resulting search terms are comparable regardless of whether they are pulled in the same round. This approach is based on Abay et al. (2020).

The Google mobility index shows how visits to places are changing over time in each geographic region. Google mobility data is measured against a baseline established by Google

> 5 https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG

> 6 “Sentinel-5P NRTI NO2 :
|31%|31%|31%|30%|29%|27%|25%|||

Source: DHS surveys for 1993, 1996/97, 1999/20, 2004,2007, 2011, 2014, and 2017. Note: For each age group and birth cohort, i.e. for each ‘cell’, this table shows the number of (unweighted) observations (N) and the share (as percentages) of women who are employed in Bangladesh. Reading the table along the column provides an approximation to the age effect, i.e., what happens to the employment rate of women from a specific birth cohort as they age. Reading along the rows, the table provides an approximation of the cohort effect, i.e., the differences in the employment rate among cohorts observed at the same age. The last row, “weighted average”, represents the employment rate for a specific cohort average along its whole life cycle. The weights from the different surveys as well as the size of the cell are used in the calculation.

34
# **1. Introduction**

A large literature on the determinants of poverty finds a strong association with regional and country-specific factors, like the vulnerability to flooding or tropical diseases, remoteness, quality of governance, and property rights (Dollar et al., 2016). A second strand of the literature finds within-country factors, such as the availability of infrastructure (roads, water, electricity) and services (health, education), proximity to markets, and social relationships, to matter as much (Kraay and McKenzie, 2014 for example). A third set of studies investigates household characteristics, such as household size, age structure, dependency ratio, gender of the head of household, employment status, hours worked, property owned, nutritional status and educational attainment, and correlates those to the poverty headcount (Banerjee and Duflo, 2011).

In this paper, we disentangle the link between business regulations and their enforcement and the poverty headcount, expanding the evidence on country-level determinants of poverty. In particularlar, we use business regulations on starting a business, acquiring licenses, getting credit and contract enforcement, as well as an overall regulatory ranking as indicators of property rights. The data are constructed from the World Bank’s _Doing Business_ project and cover 189 economies from 2005 to 2013.

We find that business-friendly regulations are correlated with lower poverty headcount, as is higher government expenditure, a country’s income per capita, and the regional dummies for East Asia and Eastern Europe. We suggest that the conduit for poverty reduction is business creation, both as a source of new jobs and as a manifestation of thriving entrepreneurship.

The rest of the paper is organized as follows. Section 2 describes the data sources. Section 3 details the empirical strategy and presents the main results. Section 4 concludes.

# **2. The data**

This section describes the sources of the dependent variables, and the sources and construction of independent variables.

The main dependent variable used in previous studies is the poverty headcount
other major cities of 38 to 115,000 residents, and 26 districts (most of which have between 25,000 and 65,000 residents). The persistence of unemployment is suggested by the fact that the correlation between end-of-year unemployment rates in 1993 and 2002 across 33 NUTS 3 regions is 0.77. The correlation in year-on-year unemployment rates varies from 0.925 to 0.991 in the 1995-2002 period.

Appendix Figure 9 and Appendix Tables 12 and 13 show that overall disparities in local unemployment increased sharply until 1994, fell between 1994 and 1998, and rose again in 1999-2001. These trends were driven by trends in districts with above-average unemployment. This pattern holds true whether or not weights for the size of the local labor force are taken into account. The picture does not change either when the 7 major cities of Latvia are included in the adjacent districts. Appendix Figure 9 also charts the evolution of the “unemployment Gini index”, which is calculated in the same way as the usual “income Gini” and measures inter-regional inequity of distribution of unemployed persons in country’s labor force (ignoring inequity within districts in question). Recent values of Gini (0.31) are quite close to Latvia’s income Gini (0.34 in 2001). This Gini value suggests a much more uneven distribution of unemployment than, say, in Lithuania. According to this measure, the Lithuanian unemployment Gini is about 0.20.

Persistence in wages has been even stronger than that of unemployment. Year-on-year wage correlations across districts vary between 0.954 and 0.992. The correlation for 1992-2002 is 0.92. The overall level of disparities, as measured by Gini or weighted coefficients of variation, fell in 1993-1995. Since then, inter-district wage disparities appear to have remained stable. This trend is shown in the left bottom panel of Appendix Figure 9.

Still, non-weighted measures of inter-regional wage disparities, which do not account for variation in the number of workers in regions, show
<!-- Start of picture text --> 10.00%<br>8.00% 7.6%<br>6.80%<br>6.00%<br>4.19<br>4.00%<br>1.809<br>2.00%<br>0.00% 0.7%. -0.8%<br>som ——.--peer 59 ee cdweet -1.7%<br>-4.00%<br>2004-1993 2011-2004 2017-2011 2018-2017 2019-2018<br><!-- End of picture text --> — Casual wage growth ——Poverty reduction per year (NSS) --- Poverty reduction per year (NSS-2011/CPHS)
86|0.49|
|N|948|948|943|921|
|Log Likelihood|-1080.04|-1072.31|-1059.94|-1166.18|
|Akaike Information Criterion|2180.09|2168.61|2145.88|2358.36|

Note: P-values are given below coefficient estimates.<sup>†</sup> indicates statistical significance at 15%, * at 10%, ** at 5%, *** at 1%. The Pagan-Harding measure of commodity prices is based on the Pagan-Harding algorithm (2002) which identifies turning points in a times series as local minima and maxima. These are used to identify up-cycles (when gold and copper prices are rising). Higher values of the Corruption indicator correspond to better outcomes (i.e. lower corruption) as do higher values of the ICRG Quality of Government indicator. As interaction terms are nonlinear, the combined impact of these is shown separately.

16
Zambia||.|||||
|Zimbabwe|||||||

Explanation:

* Investigations based on WTO database. Frequency ratio relative to total tariff lines

** Number of Products, based on TPR's and WTO notifications

x = 2 tariff lines Blanks means zero

** _Source: GA1T, TPR; wTO, TPR; WTO. Antidumping Measures Database._

most frequent used remedy both by developed and developing countries (Miranda et. al.1998; Finger and Schuknecht, 1999). The countries in our sample taking such action increased from 11 in the period 19891994 to 18 in 1995-1998. They include most of the developing country members of the WTO which have taken anti-dumping actions.)<sup>0</sup> The Table also shows that only four developing countries out of the 42 in our sample have used safeguard actions and only six countries have taken countervailing actions, mostly in the period since 1994; and in each instance affecting only a few isolated products.

The average frequency ratio for anti-dumping actions taken by developing countries, which had taken action in both periods, also increased over the period, from an average of 1.75 per cent of tariff lines to 3.29 per cent. Despite increases in anti-dumping actions by developing countries, a comparison with Table 10 and Table A-2 in the Appendix (keeping in mind some of the methodological and data

> <sup>0</sup> ° Recent notifications for 1998 sugges **t** hat the number is increasing. In addition to the countries above, Guatemala, Trinidad and Tobago, Nicaragua, Equador and Panama have initiated investigations since 1996, the last three for the first time in 19971998, (WTO, 1999a).

29
the labor market, or supply responses to the maize price changes, such as switching to other crops or investing less on agriculture inputs.

# **4. Data and Descriptive Statistics**

We secured and organized monthly price data from more than 150 markets across Malawi and its neighboring countries of Zambia, Tanzania, and Mozambique from government sources as well as from FEWS NET. The prices are at the retail level, except for Tanzania, wherein we have data only on wholesale prices. The starting time for the price data varies by country, but we have consistent data for all four 10
incomes or consumption. They have to be measured using the same or similar methodology, and need to include as many countries of the world as possible. Perhaps at least 120-130 surveys are needed in order to cover more than 90 percent of the world population and account for 95 or more percent of world income.<sup>3</sup> Ideally of course we would like to have surveys from all the countries in the world. This is a very hard requirement. There are still quite a few countries, mostly in Africa, where household surveys are not regularly conducted and where methodologies change, rather brusquely, from one survey to another, thus rendering comparisons difficult.

Because the calculation of global inequality relies on household surveys, we cannot calculate Inequality 3 with much precision for the period before the mid- or late-1980s. There are simply no household surveys available for too many parts of the world. The first available Chinese household surveys are from 1982, the first usable surveys from the former Soviet Union are from 1988, and for many Sub-Saharan African countries, the earliest household surveys date from the mid-1980s. Thus, for the past, we have to rely on much more tentative data, where countries’ income distributions are only approximated, using various more or less reliable methods. This is particularly so if we wish to study global inequality in the long-run, covering the 19<sup>th</sup> century as well—a topic which I will discuss in Section 3 below.

Figure 2 displays the movements of the three types of inequalities after the Second World War. The Gini coefficient is on the vertical axis. Inequality 1 was stable from 1960 to 1980. This means that there was no systematically faster or slower growth of poor or rich countries. Neither were poor catching on with the rich, nor were the two groups growing further apart. Divergence started only at the beginning of globalization, around 1980, and went on until the turn of the century. These two decades were very bad
560<br>|8.44|<br>881|<br>9,231|9.55|
|Stone, Clay and Glass Product<br>|s 15,365<br>|<br>84,899<br> <br>|18.10<br>|<br>3,130<br> <br>|<br>10,817<br> <br>|28.93<br>|
|Metal products<br>|31,176<br>|<br>470,798<br> <br>|6.62<br>|<br>4,289<br>|<br>52,617|8.15|
|Electrical Machinery<br>|15,437|<br>211,184|7.31|<br>2,020|<br>27.369|7.38|
|Machinery and Transport<br>|||||||
|Equipment<br>|44,231|<br>513,071|8.62|<br>6.996|<br>79,087|8.85|
|Other|8,109|132,811|6.11|<br>1,295|<br>15,099|8.58|
|Total<br>|257,012|2,888,112|8.90|<br>41,039|328,343|12.50|

- a) the footwear industry (2,675.9 million cruzeiros) was reallocated from wearing apparel to footwear and leather.

- Note: The breakdown between food products and beverages for the US on a national accounts basis was assumed to be proportionately the same as on a US Census basis (1975 figures derived from Annual Survey of Manufactures).

Source: Brazil value
(the median distance across EAs) and infinite serial correlation.

Furthermore, there is the potential presence of spatial correlation due to omitted weather variables (Auffhammer et al., 2013). To account for this problem, we construct a measure of drought during the agricultural season, using the Standardized PrecipitationEvapotranspiration Index (Vicente-Serrano et al., 2010; Miao and Popp, 2014; Harari and Ferrara, 2018).

22
the infrequently visited household diary, it will lie between US$280 and US$334. Clearly the diary approach, especially the benchmark of a personal diary, is a much more resource intensive form of consumption measurement, roughly six to ten times the cost of administering a recall survey to the same household. The cost-savings from a frequently supervised household diary is a relatively modest 25 percent of total personal diary costs. Given the performance of the household diary, especially in urban areas, these resource savings may not be worth the trade-off in terms of downward biases in consumption measurement.

# **6. Conclusions**

In most developing countries, consumption expenditures measured by household survey is the empirical basis for studying poverty and inequality. Yet there is substantial variation both within and across countries in the design of consumption modules. These differences result in challenges to the cross-population comparisons of poverty and living standards as well as efforts to study changes in poverty over time. This study explores the implications of alternative consumption modules design in Tanzania. We designed and fielded eight alternative surveys, selected to capture the most common designs in practice. We benchmark our comparisons to frequently-supervised personal diaries, which we assume come closest to measuring actual consumption by accounting for personal outside-the-house consumption as well as minimizing recall and telescoping error due to the frequent supervision. This method is, however, impractical for large-scale survey work due to high costs – we estimate that it will cost roughly 6 to 10 times as much as a recall format, and roughly twice as much as infrequently supervised household diaries.

With regard to diaries, the quality of reporting in household diaries did not vary a great deal with the frequency of field staff visits designed to minimize recall and telescoping error. This is true for all consumption categories (although food consumption was marginally higher (6 percent higher) in the infrequent diary). One notable exception is the literacy of the household. The diary method will dramatically underestimate consumption if the household is illiterate and receives infrequent supervision. For other households
that the empirical application presented in Section 7 uses data for a group of Eastern European banks. Most of the banks in Eastern European countries are either national or regional banks, or subsidiaries of foreign banks, rather than international banks. Entries for G10 countries are also provided in the QIS5 study and can be retrieved as required.

**Table 1:** Selected Through-the-cycle Probabilities of Default (PDs) and Losses Given Default (LGDs).

|Asset Class|CEBS-|Group1|CEBS-|Group2|non-G10|-Group2|
|---|---|---|---|---|---|---|
||PDs|LGDs|PDs|LGDs|PDs|LGDs|
|Corporates|2.20|38.1|0.83|35.2|1.47|na|
|SMEs<sup>(1)</sup>|3.26|38.8|3.66|31.7|4.31|49.6|
|Consumer Mortgage Loans|1.52|21.4|1.39|21.4|17.72|40.4|
|Consumer Loans<sup>(2)</sup>|3.69|55.0|2.33|51.9|11.34|55.7|
|Other Consumer Loans|4.33|47.9|2.32|42.2|6.22|45.1|
|Sovereigns<sup>(3)</sup>|0.13|27.7|0.04|38.2|0.24|na|
|Banks<sup>(4)</sup>|0.22|39.4|0.11|39.4|0.74|na|

**Notes:**<sup>
education reforms since it usually faces with strong resistance in communities, and among parents and teachers. Therefore, a necessary pre-requisite to embark in such reforms is a strong political will and coordinated efforts of Government agencies and local authorities to materialize. A detailed roadmap with a thorough nationwide analysis and realistic investment plans should be clearly defined. And public campaigns need to be incorporated as an essential

> 34 In a first attempt, Thoryk (2011) uses 2007 TIMSS data to estimate the effect of school size on school performance in transition countries. Overall his country specific estimates suggest there is little relation between school size and performance. His results for Ukraine suggest an insignificant effect of size on 4<sup>th</sup> grade science and 8<sup>th</sup> grade mathematics, an inverted U shape for 4th year math and a U-shaped relationship for 8<sup>th</sup> grade science. While the latter two results are significant, the size of the effect is found to be negligible.

55
services, such as assistance with respect to residential care, home care, community care, and rehabilitation centers. Social services can address the needs of people with disabilities by offering assistance with daily living tasks, aiding in employment endeavors, and facilitating accessibility accommodations (World Bank, forthcoming). Furthermore, multidimensional poverty is exacerbated among individuals facing additional deprivations, such as limited education, health issues, or overcrowded housing, particularly in Bulgaria and Romania. These findings highlight the complex relationship between poverty and various life domains, underscoring the need for targeted interventions addressing diverse deprivation dimensions to effectively address multidimensional poverty.

**The socio-economic and familial characteristics of multidimensionally poor individuals differ significantly from their non-poor counterparts, with Bulgaria exhibiting the most pronounced differences** . Across all countries, those at risk of poverty or social exclusion are more likely to be older, female, less educated, and unemployed, often residing in rural areas and overcrowded houses. Additionally, family structure plays a significant role, with AROPE individuals more commonly found in households with three or more children and older members. Conversely, factors such as higher education levels, employment status, and household composition involving more adults are associated with lower probabilities of experiencing multidimensional poverty. However, the impact of certain family characteristics on poverty vulnerability varies across countries, emphasizing the need for nuanced policy responses tailored to specific socioeconomic contexts. Despite common patterns, certain household types are more vulnerable to poverty in specific countries.

**Properly identifying groups to target with social policies is essential for ensuring that fiscal resources are used effectively and anti-poverty policies reach the intended population.** However, the lack of ethnic identifiers in most EU household surveys poses a considerable challenge for policy formulation and implementation.

**Our extended measure of multidimensional poverty (AROPE) indicates that about half of the population in Bulgaria, Croatia, and Romania are AROPE +, mainly because of the large percentage of individuals who are educationally deprived.** This analysis shed light on the interrelationships across multiple core dimensions of
about private savings, but not the decision of whether to place the private savings into a tax-deferred pension account.

The model is dynamic and explicitly incorporates forward-looking behavior under a rational expectations assumption. It also incorporates uncertainty and incomplete information. Specifically, individuals are uncertain about future wage shocks, future fertility, future divorce or widowhood, future survival and investment returns at the time of making labor supply and savings decisions. In solving the model, we assume that the 2008 pension reform came as a surprise and was not anticipated. We assume this, in part, because our discussions with the Budget Office in Chile indicated that the reform was not anticipated.<sup>34</sup> Thus, decisions up until 2008 are governed by a pre-reform decision model and decisions after 2009 are governed by a post-reform model. This requires solving two different versions of the model. First, we solve the dynamic programming problem (obtain the Emax values) assuming no reform and we estimate the structural model parameters only using pre-pension reform data. Then, fixing those parameters, we resolve the dynamic programming problem (i.e. obtain the Emax values that individuals use to form expectations) with the reform in place. These new Emax values are then used to simulate decision-making after the reform was introduced.

To a limited extent, the model incorporates business cycle effects in that returns on pension investments vary over time. Two limitations of the model are that investment returns are assumed to be i.i.d. and that there are otherwise no aggregate earnings shocks. However, aggregate demographic changes in the economy are incorporated in a few ways. First, the initial conditions include the education levels of the husband and wife and rising levels of education with successive birth cohorts will lead to different decision-making for different cohorts. For example, more recent cohorts of women who have higher education levels on average and the model will generate that they have fewer children and participate more in the labor force. Also, the model takes marital sorting patterns as