rafmacalaba/data-use-mentions-extended huggingface dataset:
product. And again, as with rainfall, coefficient ordering shifts across models. This is more evident with the smaller number of EO sources used in the temperature regressions. Most frequently, countries follow a trend of one set of ordering for the weather only model specification, which shifts to a different ordering for the models which include fixed effects. Consider, as an example, Niger in Figure 10: in the first panel ERA5 produces large negative and significant coefficients while MERRA-2 produces small coefficients that are either negative and significant or not significant. In the models with FE, MERRA-2 produces the largest negative coefficient while ERA5 produces the largest coefficient, now positive and significant. While we see that similar reorderings appear in other countries for other temperature metrics, the most striking element of the temperature results are that we see strong patterns in the specification charts for a couple countries, specifically Nigeria and Uganda. As we saw in the descriptive statistics, there is less variation in the temperatures that the different EO products report relative to the temperature products and thus less changes to the ordinality of coefficients.

Before moving to a closer examination of changes in ordinality in regression results, two other regularities in the specification charts bear commenting upon. First, for both rainfall and temperature, regardless of the EO product or the weather metric, weather becomes much less important in predicting agricultural productivity once household FEs are included. Over and over again, regressing output on just rainfall or temperature shows a significant relationship between the two. But once household FEs are included to control for time-invariant unobservables, the relationship goes away. Weather is a time varying phenomenon, often considered exogenous or as good as randomly determined. It is not immediately obvious why the inclusion of a time-invariant control destroys the relationship between weather and agricultural production. A likely reason, which we leave unexplored, is that weather in a given location does not vary that much over time. Thus, including household FEs, assuming a household does not move, is essentially a location FE. Once location is
# _4.2 Electricity Consumption Growth and Economic Indicators_

Despite obvious noise in the data, electricity consumption growth is strongly correlated with other measures of economic activity. Figure 3 shows quarterly changes in electricity consumption from the first quarter of FY2016 to the last quarter of FY2021 and quarterly changes in industrial production and exports. At 0.60 for industrial production and 0.66 for exports, both correlations are strong. The fourth quarter of FY2020, when economic activity was severely disrupted due to the COVID-19 pandemic, is a clear outlier with a strong decline in exports and electricity consumption.

**Figure 3:** Correlation between Electricity Consumption and Economic Indicators

a. Quarterly changes in electricity consumption b. Quarterly changes electricity and industrial production consumption and exports <!-- Start of picture text --> Industrial Production, Percent Exports, Percent<br>25 R=0.60 20 R=0.66<br>FY21 Q4<br>20<br>10 FY21 Q4<br>15<br>FY21 Q3<br>FY21 Q1<br>10 0<br>FY21 Q3<br>FY21 Q2<br>5<br>FY21 Q1 FY21 Q2<br>-10<br>0<br>FY20 Q4<br>FY20 Q4<br>-5 -20<br>-20 -10 0 10 20 30 -20 -10 0 10 20 30<br>Electricity consumption (% change) Electricity consumption (percent %)<br><!-- End of picture text --> Changes in electricity consumption can be used as a proxy to infer insights on the economic impacts of shocks, for example, the COVID-19 pandemic, associated lockdowns, or natural disasters. Figure 4 shows that electricity consumption declined sharply in the fourth quarter of FY2020, right after the first lockdown was imposed in Bangladesh at the end of March 2020. Electricity consumption recovered subsequently, but year-over-year growth remained minimal in the first quarter of
measurement of income changes are not easily used as inputs to the quantitative welfaretheoretic analysis, thereby constituting a problematic gap between these two literatures. Furthermore, as the literature has often pointed out, the measurement of dynamic income changes is itself confronted by (at least) the following two problems. First, income data are subject to measurement error and, second, a significant proportion of the observed income changes may be simply temporary in nature - resulting, typically, in an overestimation of the relevant mobility in income (Lillard and Willis (1978) Solon (2001), Luttmer (2002), Fields et al (2003), Glewwe (2004), Antman and McKenzie (2007)). This is also important from the perspective of welfare analysis, as measurement error has no effect on workers’ welfare and transitory shocks to income are perhaps easily smoothed out, resulting in very small welfare effects. In addition, welfare analysis is confronted by an additional challenge. Since individual utility is postulated as taking consumption rather than income as its argument, its direct valuation requires reliable data on individual consumption levels, which are often unavailable for developing countries. To use the more easily available data on incomes, a theoretical framework is required that translates the estimated income dynamics into consumption changes taking into account the institutional constraints individual agents face.

In this paper, we develop a tractable analytical framework to study income mobility that provides a close link between the welfare theory and the empirical methodology used in the measurement of the income dynamics, thereby helping to bridge the gap between these literatures. At the same time, this framework overcomes many of the methodological problems that we have just discussed. We note, at the outset, that our focus is on income mobility within individual lifetimes (intra-generational mobility). We postulate (Section II) that individuals face a stochastic income process that is highly parameterized, but, following much of the literature, is sufficiently elaborate to distinguish between changes in income resulting from trend growth and other predictable factors and changes
there is no strong evidence of convergence in agriculture and services. More importantly, their results show that the rate of convergence in manufacturing and mining has been strong enough to lead aggregate convergence across regions, albeit at a slower pace because of high employment shares of agriculture and services.<sup>8</sup> In the case of Brazil, Azzoni and Castro (2023) examined regional disparities from 2002 to 2019. Their findings indicate a general increase in _σ_ -convergence in per capita income across regions. Moreover, periods of economic crisis, such as the Great Recession of 2008 and the economic crisis of 2014, were linked to more pronounced convergence. The study also reveals evidence of conditional convergence in regional per capita income and wages (serving as a proxy for labor productivity) during these crisis periods.

Overall, this observed (or lack thereof) convergence and its speed call for further evidence that accounts for the drivers of factor accumulation, sectoral composition, technology diffusion, and production externalities within and across regions and countries.

# **3 Empirical approach**

## **3.1 Data**

Given data availability, we focus on the period from 2002 to 2018. The main dataset comes from the Relação Anual de Informações Sociais (RAIS), which is a matched employer-employee dataset put together by the Ministry of Labor and Employment (Ministério do Trabalho e Emprego). RAIS covers all registered firms and formal employees in which a firm is identified by its registration number (CNPJ) and a worker by its identification number (PIS). Most importantly, both the PIS and CNPJ do not change over time, even when a worker stops to work in the formal sector. These unique features allow us to track workers and firms over time. The dataset contains detailed information on firms’ geographic location, economic classification (CNAE 5-digit), employment size, and personnel records such as monthly wage, age, gender, and education, among others.<sup>9</sup
12 advantage in a wide range of manufactures appears significant for only one MENA country, Israel. The comparative advantage of other MENA countries in manufactures is typically in products requiring mainly unskilled labor, such as clothing and other apparel. With respect to this important category of trade in manufactures for many less developed countries, several MENA countries are identified as competitive producers of "light" manufactures: Cyprus, Lebanon, and Turkey in the Middle East and Egypt, Morocco, and Tunisia in North Africa.

The comparative disadvantage of the MENA countries in agriculture stands in contrast to their strong comparative advantage in crude petroleum. The one important exception is fruits and vegetables. With the exception of Iraq and Kuwait in the Middle East and Algeria and Libya in North Africa, most MENA countries exhibit strong comparative advantage in fruits and vegetables.

Individual MENA countries also exhibit substantial comparative advantage in selected agricultural categories and products. Jordan, Syria, and Sudan evidence international competitiveness in livestock, meats, and dairy products. Similarly, Saudi Arabia and Turkey are revealed to be competitive exporters of wheat; Jordan and particularly Egypt of rice; Cyprus, Syria, Turkey, and Tunisia of barley; Cyprus, Lebanon, Turkey, Egypt, and Sudan of sugar; and Syria, Egypt, and Sudan of agricultural raw materials.

# **Intrare2ional Trade**

The intraregional RCA statistics gauge the competitiveness of the MENA countries as exporters to one another. As might be expected, the competitiveness of the MENA countries as suppliers of not only crude petroleum but also light manufactures is reduced, reflecting the similarity of mineral fuel deposits and labor resources in most MENA countries. Of greater interest here is the evidence that the MENA countries have appreciable intraregional comparative advantage in agriculture, led in particular by strong comparative advantage in livestock, meats, and dairy products, and fruits and vegetables. This may reflect not only some sharp differences in
Concluding remarks**

This paper proposes an empirical methodology to analyze the middle class based on the notion of vulnerability to poverty. Following a regression-based approach this paper exploits panel data to estimate the amount of comparable income –$10 dollars a day, associated with a low (0.10) probability of falling into poverty— which depicts the beginning of the middle class (lower threshold). In the case of the upper threshold it is established at $50 dollars a day. By doing so, this paper proposes an absolute-standard definition of the middle class as those households living with a per capita income of $10-50 a day at 2005 PPP. These thresholds are then used in cross-sectional surveys to measure the size of the middle class in Chile over 19922009, in Mexico over 1992-2008, and in Peru over 1997-2009.

Following this definition, this paper overcomes the two main shortcomings that emerge when comparing middle classes across countries using existing measures. First, relative measures face the problem of different income or consumption distributions from one country to another, and thus they compare different middle classes. Second, the use of very low absolute thresholds in upper-middle income countries, such as Chile, Mexico and Peru, includes a high proportion of poor households within the middle class definition. This sheds light on the descriptive analysis that emerges from the use of monetary thresholds without a conceptual framework. Overall, this paper makes two important contributions to the measurement of the middle class. First, methodologically, it ensures that no lower class or poor

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Cognitive Function in Infancy.” _Pediatrics_ 126 (2): e427–34. https://doi.org/10.1542/peds.2009-2097.

Dai, Haijiang, Tariq A. Alsalhe, Nasr Chalghaf, Matteo Riccò, Nicola Luigi Bragazzi, and Jianhong Wu. 2020. “The Global Burden of Disease Attributable to High Body Mass Index in 195 Countries and Territories, 1990–2017: An Analysis of the Global Burden of Disease Study.” _PLoS Medicine_ . https://doi.org/10.1371/journal.pmed.1003198.

Daru, Jahnavi, Javier Zamora, Borja M. Fernández-Félix, Joshua Vogel, Olufemi T. Oladapo, Naho Morisaki, Özge Tunçalp, et al. 2018. “Risk of Maternal Mortality in Women with Severe Anaemia during Pregnancy and Post Partum: A Multilevel Analysis.” _The Lancet Global Health_ 6 (5): e548–54. https://doi.org/10.1016/S2214-109X(18)30078-0.

Dávila-Cervantes, Claudio Alberto. 2020. “Cardiovascular Disease in Mexico 1990-2017: Secondary Data Analysis from the Global Burden of Disease Study.” _International Journal of Public Health_ 65 (5): 661– 71. https://doi.org/10.1007/S00038-020-01377-4.

Donkor, William E.S., Seth Adu-Afarwuah, Rita Wegmüller, Helena Bentil, Nicolai Petry, Fabian Rohner, and James P. Wirth. 2021. “Complementary Feeding Indicators in Relation to Micronutrient Status of Ghanaian Children Aged 6–23 Months: Results from a National Survey.” _Life_ 11 (9). https://doi.org/10.3390/LIFE11090969/S1.

Eaton, Jacob C., Pamela Rothpletz-Puglia, Margaret R. Dreker, Lora Iannotti, Chessa Lutter, Joyceline Kaganda, and Pura Rayco-Solon. 2019. “Effectiveness of Provision of Animal-Source Foods for Supporting Optimal Growth and Development in Children 6 to 59 Months of Age.” _Cochrane Database of Systematic Reviews_ 2019
with 5 or more employees and cover manufacturing and services firms largely collected via face-to-face interviews with business owners or top managers. Our sample for the analysis is restricted to firms that have geo-located information. The final sample consists of about 88,000 firms (depending on the specification) across 174 surveys over 118 economies in the time period 2009-2019. Summary statistics are provided in table A1. The full list of countries and survey years are presented in table A2. In addition, for countries where there were several rounds of surveys, we can track whether the firm exited the market regardless of whether they were re-interviewed in successive waves. We can obtain information on firm exit across 55 countries.

The ES methodology includes a consistent definition of the universe of inference, a standard sampling methodology, a standardized survey instrument, and a uniform methodology of implementation. The selection of firms in each country is achieved by stratified random sampling with three levels of stratification: sector, size, and location within the country. Sampling weights are used to correct for unequal probability of selection, ineligibility and non-response. The data are largely collected using ComputerAssisted Personal Interviewing (CAPI) software. The CAPI software collects geo coordinates of the firm’s location that we use to match rainfall and temperature data with the firm-level data. To maintain anonymity of the respondents, the geo-codes are masked around a 2km radius.

The surveys are implemented uniformly across countries. Formal training sessions of supervisors and enumerators are undertaken to ensure the best practices are employed consistently. Quality control checks are implemented to guarantee the quality of the data throughout the data collection process. Consistency checks are employed for 10% and 50% batches of the data during the survey to facilitate callbacks to respondents to be undertaken when necessary to verify information. Information on the Enterprise Surveys global methodology and on the sample design and weights computation is available on the website http://www.enterprisesurveys.org. The data have been widely used by several studies analyzing the private sector in developing economies (Besley
show that such a model indeed describes the Irish experience with FDI. Alfaro and Rodriguez-Clare (2003) use plant level data from several Latin American countries and they make the important point that many empirical studies testing existing theoretical models often use inappropriate measures to evaluate the linkage effects of multinationals. More specifically, empirical studies often use the share of inputs purchased locally by a multinational to measure its impact on linkages. They argue that requirements, may have costs above world prices, raising the possibility that greater linkages imply lower GDP per capita.

24
S&P Rating|0.181**<br>(0.082)||0.176**<br>(0.083)||
|Relative Rating||0.200**<br>(0.079)||0.202**<br>(0.080)|
|U.S QE indicator|1.814***<br>(0.381)|1.728***<br>(0.383)|||
|U.S Fed Purchases|||0.005**<br>(0.002)|0.004**<br>(0.002)|
|Growth diff.|0.215**|0.214**|0.232***|0.231***|
||(0.087)|(0.087)|(0.087)|(0.088)|
|Interest rate diff.|0.471***|0.487***|0.442***|0.462***|
||(0.116)|(0.117)|(0.116)|(0.117)|
|VIX|0.035<br>(0.039)|0.028<br>(0.039)|0.028<br>(0.039)|0.023<br>(0.039)|
|Country fixed effects|Y|Y|Y|Y|
|No. of obs.|859|859|859|859|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.255|0.257|0.243|0.245|

Notes: The post-crisis sample includes quarterly panel
br>4<br>6<br>8<br>10<br>12<br>wth in real output (%)||
|---|---|---|
|0|o||
|1987<br>1989<br>1991<br>1993<br>1995<br>1997<br>1999<br>2001<br>2003<br>2005<br>2007<br>2009<br>2011<br>2013<br>2015<br>2017|-2<br>0<br>nnual gr|1988<br>1990<br>1992<br>1994<br>1996<br>1998<br>2000<br>2002<br>2004<br>2006<br>2008<br>2010<br>2012<br>2014<br>2016<br>2018|
|Agriculture<br>Mining<br>Manufacturing|A||
|Construction<br>Trade<br>Transport|-4||
|Business<br>Other|-6||
|Source: Authors’ calculations based on data from DOSM<br>**Table 4: Average annual growth in output by sec**<br>**Average annual growth in output**<br> <br> <br>|Source:<br>**tor, 1987-**<br>**(%)**<br>|Authors’ calculations based on data from DOSM<br>**2018**<br>**Average annual growth in output**<br>**(excluding crisisyears)**<sup>**♠**</sup> **(%)**<br> <br> <br> <br>|
|**1987-1997**<br>
# **Appendix A. Historical Financial Data Cleaning Procedure**

Following Bureau van Dijk (2011), Kalemli-Ozcan et al. (2015), Cusolito and Didier (2020), and Kalemli-Ozcan et al. (2023), we document the steps we apply to clean the financial information.

1. _Fill time-invariant data gaps_ : for a given BvD.ID-year combination, with BvD.ID standing for firm unique identifier, replace missing highly likely time-invariant information with information available for previous years (e.g., US SIC code, NAICS, NACE, NACE main sector, company name, city, region, postal code, legal form, incorporation date). To perform this step, the team first worked with auxiliary raw tables, which collect legal and sectoral information of the firm, and collapsed the time-invariant variables at the BvD.ID level.

2. _Harmonize timeframe_ : convert variable closedate from string to numeric format. Then create a new variable, name it year, and assign a year to the observation according to the following rule. If closing month corresponding to the observation is June or any other month after June, then make Year take the year reported in closedate. Otherwise, make Year the year reported in closedate minus 1.

3. _Drop duplicates_ : the raw database presents a large number of duplicates at the BvD.ID-year level. The team noticed that the information was the same, except in the SIC primary code variable. Thus, we collapsed all the SIC primary codes reported by the same BvD.ID-year in one variable, using semicolons to list all the SIC primary codes, and eliminated duplicates.

4. _Drop firms with missing relevant information_ : drop all the firms with no information for the following set of variables: US SIC code, NAICS, NACE core code, NACE main sector.

5. _Drop observations with missing information for the currency code_ : eliminate observations with missing information for the currency code.

6. _Drop observations with missing information for variable closedate_ : eliminate
variations in health are probably greatest and that drop-out of interviewers is a potential problem in such a long fieldwork period (Harpham 1987). Moreover this approach would not provide the individual-level data on seasonal variations in health needed to investigate this subject fully.

# 5.9 Validation and examinations

Laboratory investigations in communitv-based studies can for logistical and ethical reasons be divided broadly into those which assess and evaluate the delivery of health care services and those which examine health and nutritional status. The first group would include capillary blood samples taken for measurement of antibodies against tetanus, measles, and polio etc. In such studies blood samples would be collected as part of a sLngle round cross-sectional survey and brought, after suitable preparation 'in the field', to a central laboratory. There would be no need for immediate feedback of the information to the individual in order that treatment might be obtained. The second group would include capillary blood samples taken for measurement of haemoglobin or thyroid status etc where early treatment for those 'at risk' with very abnormal values would be indicated. This ethical consideration has been facilitated by the recent development of accurate, simple reproducible means for measuring haemoglobin 'cn site'; severe anaemia for instance could be treated immediately. The remainder of the blood sample could then be taken to the base laboratory.

# 5.10 Data vrocessing

Data processing problems are a most serious issue in manv countries. The analysis and dissemination of survey results are often delaved because all aspects of data processing have not been determined before the collection of data starts. However, there is now experience of using microcomputers and appropriate software to overcome such problems. The organisation of the Living Standards Measurement Studies (LSMS) of the World Bank is a useful example. The LSMS in the Ivory Coast (1600 households) produced a preliminary abstract of results just two months after the end of fieldwork (Spitzer 1986). This was achieved by making microcomputers the survey's hub around 42
database of global administrative areas—ADM1, corresponding to states or governorates, and ADM2, corresponding to districts, municipalities, or communes—using their center points (“centroids”). The baseline specification to be estimated by ordinary least squares (OLS) is where _i_ and _k_ are indices for cell and country, respectively; EverAid _ik_ is a binary variable that equals 1 if cell _i_ had at least one active aid project in the period of study and equals 0 otherwise; Mission _ik_ is an indicator that equals 1 if at least one historical mission was located in cell _i_ and equals 0 otherwise.

The baseline estimation exploits only within-country variation. The inclusion of country dummies _δk_ controls for all time-invariant country-level characteristics, many of which are important determinants of foreign aid.<sup>8</sup> To test for robustness, additional specifications include dummies for subnational administrative divisions (ADM1 or ADM2) instead of country-level dummies.

The vector **X** _ik_ consists of control variables at the cell level, including a number of historical and geographical factors described in Section 2.2. The error term _εik_ is allowed to be spatially correlated within a radius of 220 kilometers (approximately four times the side length of a cell) from the centroid of each cell. This means that clusters are unique to each cell and that a typical landlocked cluster covers about 45 contiguous cells.<sup>9</sup> Clustered standard errors are calculated using the estimator developed by Conley (1999).<sup>10</sup> This type of standard error is appropriate because missions are highly clustered in the data (see fig. 1).

The coefficient _β_ has a causal interpretation if and only if **X** _ik_ and _δk_ contain all relevant determinants of mission locations that are also correlated with present-day aid allocation. Failure to include important controls will bias the size of the coefficient. The most obvious threat to a causal interpretation of _β_ is nonrandom selection of mission stations. Missions