rafmacalaba/data-use-mentions-extended huggingface dataset:
300,000|> 50%|> 50%|
|Urban-Metro (small)<br>< 300,000|> 50%|> 50%|
|Urban (large)<br>≥ 50,000|> 50%|≤ 50%|
|Urban (small)<br>< 50,000|> 50%|≤ 50%|
|Rural (large)<br>≥ 30,000|≤ 50%|≤ 50%|
|Rural(small)<br><30,000|≤ 50%|≤ 50%|
|**Colombia**|||
|Urban-Metro<br> <br>|All designated as metro<br>|areas in 2011<br>|
|Urban (large)<br>≥ 20,000|> 50%|Nonmetro areas|
|Urban (small)<br>< 20,000|> 50%|Nonmetro areas|
|Rural (large)<br>≥ 10,000|≤ 50%|Nonmetro areas|
|Rural (small)<br><10,000|≤ 50%|Nonmetro areas|
|<br>**All other LAC countries**|||
|Urban|> 50%||
|Rural|≤ 50%||

We explore the variation in productivity across different types of 2<sup>nd</sup> level administrative units, defined using an urban gradient that varies with the size of the country and territorial coverage of the data. In the cases of Brazil and Mexico, using the rich information in the IPUMS population censuses, it is possible to distinguish between six types of municipalities: large metropolitan areas; small metropolitan areas; large urban areas; small urban areas; large rural areas; and small rural areas. Table 1 lists the criteria for defining the different types of localities in each country case. For example, in Mexico, large metropolitan areas
has an overall lesser average growth rate, and is more coarsely gridded. With respect to the latter point, as Donaldson and Storeygard (2016) note (p. 192), though there may be a tendency to equate resolution with information value, this may not always be the case; as we will show, this difference in grid size does not diminish the explanatory power of NO2 versus lights at the country-level. Moreover, other NO2 sensors present and future have higher resolution if a more

> 5(Lamsal et al., 2021). Alternative ways to access the complete raw data set, such as to compute averages over all country area used later in this paper, are available through NASA’s Earth Science Data Systems (ESDS) at `https://earthdata.nasa. gov/` and GES DISC tool at `https://disc.gsfc.nasa.gov/` . Visualization tools are available at `https://worldview. earthdata.nasa.gov/` and `https://giovanni.gsfc.nasa.gov/giovanni/` .

> 6We discuss the construction of the night lights data set we use, along with all technical definitions, in Online Appendix B.1. In short, we use inter-calibrated DMSP-OLS data until the end of 2013, when the improved stray light-corrected, stable-lights VIIRS product becomes available (see Mills et al. (2013) for this data product and Gibson et al. (2020) for why VIIRS is preferable to DMSP-OLS when available). With respect to GDP, the most recent vintage of the Penn World Tables v. 10.0 ends in 2019. To compute GDP in 2020, we used World Bank data on 2020 growth for all 81 countries with data available at the time of writing. Thus, we consider an unbalanced panel.

6
were significantly outdated: Republic of Congo (1994), Côte D’Ivoire (2005), Ethiopia (2007), Guinea (2006). In Mali, 2020 data is available for the direct-to indirect tax ratio, but income tax data is only available for 2012, so results are based on 2012 data for all three indicators (the direct-to-indirect tax ratio is very similar for 2012 and 2020).

26
# Figure 11. LAC: Output growth and drivers of potential growth

A. GDP growth

# B. Potential GDP growth

<!-- Start of picture text --> Percent Percent<br>10 LAC Median Range for EMDE regions 8 LAC Median Range for EMDE regions<br>8<br>6<br>6<br>4 4<br>2<br>2<br>0<br>-2 0<br>2000-21 2000-10 2011-21 2022-23 2000-21 2000-10 2011-21<br>C. Potential growth by different measures D. Potential TFP growth<br>Percent Percent<br>4 4<br>2000-10 2011-21 LAC Median Range for EMDE regions<br>3 3<br>2 2<br>1 1<br>0 0<br>PF MVF UVF Exp. 2000-2021 2000-2010 2011-2021<br>E. Investment growth and changes in F. Investment growth<br>terms of trade<br>Percent Percent<br>Investment Terms of trade<br>15 14 LAC Median<br>10 12<br>5 10<br>0 8<br>-5 6<br>4<br>-10<br>2<br>-15<br>0<br>2000-21 2000-10 2011-21<br>Sources: Haver Analytics; National statistical agencies; Penn World Tables; UN Population Prospects; World<br>Bank.<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates). A.B.D.F. Bars show period averages of annual GDP-weighted averages. Markers show the median of GDPweighted averages of the six EMDE regions; orange whiskers show min-max EMDE
first studies to estimate this parameter, Katz and Murphy (1992) find a value of around 3. Card and Lemieux (2001), arguably one of the most influential papers on the extent of substitutability between young and old workers, estimate a, across three countries. Using data on the US, Canada and the UK, they find values between 4 and 6. In more recent studies, Manacorda et al. (2012) estimate a value of around 5 using UK data, Ottaviano and Peri (2012) find a similar value using US data and, Glitz and Wissmann (2016) estimate values greater than 7 using German data. We set 04 = 5 in our main scenario, which is in the mid ~~-r~~ ange of these available estimates. Compared with our central estimate of og, this assumption implies that workers in different age groups within the same skill group are more substitutable than workers in different skill groups, although still imperfectly so. We later allow for different values of 0, in order to explore the sensitivity of our simulated labor market effects to the degree of substitutability between age groups.

3. Elasticity of substitution between natives and migrants (ay,). The elasticity of sub ~~-~~ stitution between migrants and natives, o),, is a crucial parameter in determining the effect of immigration on the labor market outcomes of natives. As such, this parameter has received a fair share of attention in the literature. On one hand, Borjas (2003) suggests perfect substitutability between immigrants and natives, im ~~-~~ plying a value of oj equal to infinity. Card (2009), Ottaviano and Peri (2012) and Manacorda et al. (2012) find values of oy, between 6 and 20. In our empirical analysis, we set oj, to 20, the value DOP use in their intermediate scenario. We again examine the implications of perfect substitutability on labor market outcomes in the extensions section.

4.
- 43 -

a proxy variable measuring the gap between actual output and its long-term. trend (independent variables). The estimated model is specified in equation 1 below, where AFX stands for the absolute change in the exchange rate, ?r stands for domestic inflation, **_;us_** stands for the international (US) inflation rate, rg'pis the proxy used for overheating,<sup>54</sup> and c is an error term. The estimation results using monthly data under alternative specifications of the model are reported in Annex 3. The important result drawn from this exercise, as discussed in Annex 3 is that, starting in early 1992, the authorities partly abandoned the policy of correcting the exchange rate for changes in inflation differentials and excessive output growth. In other words, during 1992-96 the policy of keeping an almost constant nominal exchange rate was strengthened, further decreasing the perceived exchange risk and therefore giving more incentives to borrow short-term from abroad.

In sum, the Thai authorities, concerned with preventing an appreciation of the real exchange rate to retain Thailand's export competitiveness, maintained a pegged exchange rate system and intervened in the foreign exchange market to maintain the peg in the face of the large capital inflows. By preventing the nominal exchange rate from departing significantly from the central peg (in particular, by preventing the exchange rate from appreciating), the policy resulted in a fairly predictable nominal exchange rate, which reduced foreign exchange risk faced by investors and increased the incentives of domestic residents to incur in unhedged, short-termn foreign debt. Hence, a more flexible exchange rate policy could have reduced capital inflows and thus overheating pressures, obviating the need to tighten monetary policy in the first place and avoiding the perverse cycle of tight monetary policy, resulting in an increase in interest rates, attracting additional inflows, which result in further tightening of monetary policy.<sup>55</sup>

> 54 The proxy variable for _overheating_ is based on the consumption of electricity in the
Policy Research Working Paper 9259

# **Abstract**

This paper identifies gaps in availability, access, and quality of household budget surveys in the Middle East and North Africa region used to measure monetary poverty and evaluates ways to fill these information gaps. Despite improving public access to household budget surveys, the availability and timeliness of welfare data in the Middle East and North Africa region is poor compared to the rest of the world. Closing the data gap requires collection of more HBS data in more countries and improving access to data where it exists. However, when collection of consumption data is not possible, a variety of other second-best strategies can be employed. Using imputation methods can help to measure monetary poverty. Constructing non-monetary poverty and asset indexes from less robust surveys, using non-traditional surveys such as phone surveys, and “big data”—administrative records, social networks and communications data, and geospatial data—can help substitute for, or complement data from existing traditional survey data.

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 aatamanov@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 for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced
minimum sample size of around 1,000 observations for the base survey (slightly over 20 percent of the sample), irrespective of the model being used. Given these samples, both the estimates and their 95 percent CI almost all fall inside the 95 percent CI around the true rate. The finding that the minimum sample size for the target survey is around 1,000 observations is consistent with previous evidence (Dang and Verme, 2023) and corroborates the findings in Section 4.

# **6. Conclusion**

We offer fresh randomization evidence on the effects of survey design on poverty imputation, on which very few studies currently exist. We implemented a randomized experiment in Tanzania with three treatment arms: (TA 1) a standard, full consumption questionnaire; (TA 2) a light questionnaire variant that includes a reduced list of food items; and (TA 3) an alternative light questionnaire variant that includes more aggregated food and non-food consumption modules, which we combine with rich household consumption survey data to provide imputation-based poverty estimates.

We find that we can obtain reasonably accurate results for quite a few imputation models when imputing either from the TZNPSs into the full consumption questionnaire (TA 1) or other lighter questionnaires (TAs 2 and 3), or from TA 1 into the other lighter questionnaires (TAs 2 and 3). Interestingly, imputing into the light questionnaire modules (TAs 2 and 3) using the less-than-ideal consumption, with the variables in TA 2 and TA 3 being standardized by those in the base survey, helps significantly improve accuracy. Our estimation results are robust to the choice of base surveys used for imputation model estimation; different poverty lines; and alternative (quarterly or monthly) CPI deflators. Further, our approach to imputation performs better than competing methods, including multiple imputation and a range of machine learning techniques. There is some meta-analysis evidence pointing to imputation accuracy gains when we employ the poverty line 26
The final characteristic in Table 6 is a measure of the asset wealth of the household. We see that consumption reporting with respect to the benchmark diverges dramatically depending on whether we look at the recall or diary format. All recall formats have a significant positive interaction term with the household asset index, implying that asset-poor households significantly under-report recall consumption vis-à-vis asset-poor households with the benchmark module, while asset-rich households report roughly equal or even greater amounts of consumption than asset-rich benchmark households. The asset index is a normalized mean-zero standard deviation-one random variable, which implies that rich households with an index score of 1 to 2 achieve consumption parity with rich benchmark households, depending on the particular module administered. This finding suggests that net recall error may be particularly acute among poorer households. If this is the case, then recall modules may actually overstate the degree of consumption inequality in a population relative to actual consumption. This possibility is explored further below.

Household diary performance interacted with the household asset index yields qualitatively different findings from that for recall, although this conclusion is more tentative given the relative lack of estimate precision. For both household diary types, the interaction terms are negative, and statistically significant for the infrequently supervised diary for select consumption subcomponents. Household diaries diverge from actual consumption as the wealth of the household increases, likely due to the increasing importance of private information about consumption as households grow wealthier. Where recall surveys may overstate the degree of inequality in the population, household diaries may actually understate it.

# _Distributional Measurement_

The results so far have focused on mean differences in measured consumption but it is possible that module type also affects distributional measures, as suggested in the sub-section above. Table 7 reports the Gini coefficient and its jackknife standard error for each module over the four consumption categories. When looking at total per capita consumption, inequality for the diary-based modules is lower than for the recall modules, although the differences are not always statistically significant. The same general pattern persists when looking at food consumption and the differences between recall and diaries
. Time series for these variables can be obtained using USDA corn usage and production data. They are then integrated into a market clearing equation with no storage decisions. The short run case considers demand as a whole aggregate and assume identical isoelastic functions for both demand and supply, as before. The model is linearized, calibrated and simulated with real shocks as inputs. It then estimates the impact of di¤erent shocks (demand, supply, and information) on price changes and quanti...es them.

One additional analysis that results from this paper is related to the issue of prices’being fully informative and appropriate for business-cycle measurement. Romer (2006) raises this question and shows evidence of ...rms that do not always behave in pro...t-maximing ways by analyzing the case for professional football teams. This paper shows that although in the long run, prices seem to be very close to fundamentals, when going to a quarterly frequency, market prices can sometimes be very far away— as much as 50%— from them. These results are in line with Hussman (1992), who shows that once imprecise signals are introduced in a rational expectations framework, market prices become 4
- Gronberg, Timothy J. and W. Robert Reed (1994). “Estimating Workers' Marginal Willingness to Pay for Job Attributes Using Duration Data.” _Journal of Human Resources_ 29(3): 911-931.

- Gutierrez, Italo A., Krishna B. Kumar, Minhaj Mahmud, Farzana Munshi, and Shanthi

- Nataraj (2019). “Transitions between informal and formal employment. Results from a worker survey in Bangladesh.” _IZA Journal of Development and Migration_ 9(3).

- Hall, Robert E. and Andreas I. Mueller (2018). “Wage Dispersion and Search Behavior: The Importance of Nonwage Job Values.” _Journal of Political Economy_ 126(4): 1594-1637.

- Hanley, Nick, Robert E. Wright and Vic Adamowicz (1998). “Using Choice Experiments to Value the Environment.” _Environmental and Resource Economics_ 11(3-4): 413428.

- Hwang, Hae-shin, W. Robert Reed, and Carlton Hubbard (1992). “Compensating differentials and unobserved productivity.” _Journal of Political Economy_ 100: 835– 858.

- Hwang, Hae-shin, Dale T. Mortensen, and W. Robert Reed (1998), Hedonic Wages and Labor Market Search, _Journal of Labor Economics_ 16(4): 815-847.

- International Labour Organization (ILO, 2014). “Global Employment Trends 2014: The Risk of a Jobless Recovery.” <u>http://www.ilo.org/global/research/globalreports/global-employment-trends/2014/lang--en/index.htm</u>

- Kumar, Krishna, Minhaj Mahmud, Shanthi Nataraj and Yoonyoung Cho (2019). “Employer and Employee Preferences for Worker Benefits: Evidence from a

34
Assets Limited” (100% stake). <mark>In April 2010 Rosgosstrakh became 100% shareholder of R</mark> osgosstrakh – <mark>Armenia. h</mark> <u>ttp://www.armbanks.am/en/insurance-market/?org=61&lang=en, www.rgs.am</u> 12. “Sil Insurance" was registered in March 1, 2000. Shareholders are Sukiasyans family (4 shareholders, Armenia) with 80% stake and "Armeconombank" Ltd. <u>http://www.armbanks.am/en/insurance-market/?org=83&lang=en</u> _<u>,</u>_ <u>www.silinsurance.am</u>

# **III. Telecommunications Shares in Armenia**

The primary source of data was from websites of domestic telecommunication companies and severalweb sites which provide worldwide information on telecommunication 24. I defined market share as the share of total subscribers, summing up fixed-line and mobile subscribers. Then I calculated obtained shares by regions. Table 3 contains mobile phone subscription statistics by company and the number of fixed-line phone subscribers, shareholders %, and market shares by regions.

The results for market share owners of the telecomunication sector in Armenia are as follows Armenia – 0 %, CIS – 38.96%, EU – 5,73%, US – 31,7%, RoW – 23,61%.

## **Supplementary Information on Ownership Shares of Armenian Telecommunications Companies**

**ArmenTel CJSC (brand Beeline):** The Armenia Telephone Company is the largest telecommunications company in Armenia. Its 100% belongs to the second largest Russian mobile network operator VimpelCom (brand Beeline) since 2006. ArmenTel is the second largest taxpayer of Armenia (e.g. in the year 2005 15.3 billion Dram, converted approximately 28 millions euro). Armentel provides 100%
pre-EA rather than EA. For example, pre-EAs with negligible populations and geographic areas were automatically merged with their neighbor via employing the “Eliminate Small Area” tool in the preEA tool package. Manual modifications were applied to pre-EAs spanning large geographic areas with high populations.

## **3.3 Further Manual Adjustments**

The national sampling frame created using the automatic approach requires additional modifications for several reasons. First, some adjustments are needed in areas where the existing datasets were inadequate to create smaller EAs. Second, the output contains several polygons with positive populations but no settlement of residents. The pre-EAs with positive populations that clearly fall within non-residential areas such as offices, buildings under construction,<sup>14</sup> recreational centers, factories, manufacturing zones, and agricultural lands are manually adjusted.

Appendix B discusses the use of boundary information produced by the approach for creating field and offline maps to supplement the survey data collection process.

# **4 Results: A National Sampling Frame – Pre-EAs for Armenia**

This section presents the sampling frame in which the pre-census enumeration areas (pre-EAs) serve as primary sampling units (PSUs).

> 14 Note that the judgments are based on satellite images from Google Street Maps available at the time of this study, i.e., April 2024. So, settlements and residential buildings recently built might have been overlooked in the construction of this sampling frame. However, these measurement errors are more likely to be random, so this limitation should not introduce systematic bias in the frame.

15
Policy Research Working Paper 5589

# **Abstract**

This paper presents a detailed picture of how sustained growth in Egypt over 2005-2008 affected different groups both above and below the poverty line. This analysis, based on the Household Income, Expenditure and Consumption Panel Survey conducted by Egypt’s national statistical agency, compares the changes in the _static_ poverty profiles (based on growth incidence curves on a cross-section of data) with poverty _dynamic_ s (relying on panel data, growth incidence curves and transition matrices). The two approaches yield contrasting results: the longitudinal analysis reveals that growth benefited the poor while the cross-sectional analysis shows that the rich benefitted even more. The paper also shows the importance of going beyond averages to look at the trajectories of individual households. Panel data analysis shows that the welfare of the average poor household increased by almost 10 percent per year between 2005 and 2008, enough to move out of poverty. Conversely however, many initially non-poor households were exposed to poverty. As a matter of fact, only 45 percent of the population in Egypt remained consistently out of (near-) poverty throughout the period, while the remaining 55 percent of Egyptians experienced at least one (near-) poverty episode. This high mobility is not a statistical artefact: it reflects the actual process of growth. Taking high vulnerability into account is essential when designing policies to protect the poor and to ensure that growth is really inclusive.

This paper is a product of the Poverty Reduction and Economic Management Network, Middle East and North Africa Region. 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 dmarotta@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