rafmacalaba/data-use-mentions-extended huggingface dataset:
of the direction and possible magnitude of the poverty effects from climate change in Mexico, rather than actual forecasts.

The rest of this paper proceeds as follows. Section 2 introduces key definitions and data sources applied. Section 3 presents the empirical methodology followed by a discussion of the results in Section 4. Section 5 concludes.

# **2 Data**

This study is carried out in 2,069 municipalities from all states in Mexico, representing 84% of the total number of municipalities in the country. Missing municipalities are largely from Oaxaca and Puebla where climate model projections could not be fitted into smaller municipalities. Both states display pockets of high poverty (see Figure 3). However, their highly fragmented political geography, especially Oaxaca, it made unfeasible to analyze climate data (available at a resolution of 50 x 50 km approx) in some small municipalities.

The analysis uses five types of information: (i) income and geographic data, (ii) climate and weather data, (iii) poverty rates, (iv) climate change scenarios, and (v) population and output (GDP) projections. Per capita GDP and geographic controls come from the National Institute of Statistics and Geography (INEGI). Daily precipitation in millimeters and temperature come from meteorological stations and the National Weather Service (Servicio MeteorolÃşgico Nacional - SMN). Historical Climate data were aggregated at the municipality level from a gridded historical dataset derived from observational data produced by the Climatic Research Unit (CRU) of University of East Anglia (UEA). These datasets were accessed through the World Bank Climate Change Knowledge Portal (CCKP)<sup>5</sup> .The CRU TS 2.1 Global Climate Dataset is comprised of 1,224 monthly time series of climate variables, including temperature and precipitation, for the period 1901-2009, and covering the global land surface, excluding Antarctica, at 0.5 degrees resolution. Poverty rates were obtained through small area estimation techniques using data from the 2000 Census on Population and Housing
(3 years) lends credence to the innovativeness and ability to capture on-ground conditions that are represented by both approaches. The near-perfect agreement between the price data from both sources is indicative of a potential to couple human-centered price monitoring system with an alternate algorithm-dependent system to evolve a hybrid highfrequency price monitoring system, bolstered by inbuilt multi-temporal validation architecture. Specifically, crowdsourced data can feed into such envisioned system as georeferenced intraday price data points, providing spatially-rich stream of training and testing data into the AI algorithm pipeline, and potentially enhancing model performance and the credibility of data outputs. Conversely, the AI algorithm can be leveraged to generate relatively coarser price data and insights at multiple administrative levels across countries (or regionally), beyond the geographic coverage of crowdsourcing efforts, especially in contexts where crowdsourcing is ineffective or yields sparse data. The value of both innovative data methods transcends mere understanding of current or past trend of commodity prices. It is also a significant milestone towards forecasting emergent food crises based on the richness of data flow to test various assumptions about price movement, investment outlook, and overall resilience or vulnerability of households to shocks and stressors (Andrée, et al., 2020; Wang, et al., 2022; Gatti, et al., 2023).

Finally, the understanding that aggregation of prices from a variety of packaging units did not compromise the relative relationship between crowdsourced and AI-estimated prices can be useful to adapt future efforts to scale either of the innovative data curation methods. Generally, the AI-estimated prices are generated for wholesale package units i.e. 100kg packages, and represents wholesale prices (Andrée, 2021a). Meanwhile, the crowdsourced price data (at level 0) were submitted based on a variety of selling packages at farmgate, retail, and wholesale market segments, and the dataset contains information that differentiates the packaging units (Solano-Hermosilla, et al., 2021; Arbia, et al., 2023). Our results align with previous finding that price per unit of commodity (i.e.
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.7. Benchmarking air transport in ECOWAS and other regional economic communities**

||ECOWAS|CEMAC|EAC|SADC|
|---|---|---|---|---|
|Annual seats, domestic ( ̳000s)|2,034|235|1,345|3,076|
|Annual seats, international within Sub-Saharan<br>Africa ( ̳000s)|362|187|1,196|964|
|Domestic city pairs served (number)|8|4|13|17|
|International city pairs served (number)|20|15|29|26|
|Seat-km in old aircraft (% of total)|43|30|33|29|
|Seat-km in recent aircraft (% of total)|57|70|67|71|
|Domestic market Herfindahl Index|0.84|0.83|0.64|0.73|
|International market Herfindahl Index|0.19|0.24|0.25|0.34|
|Market Herfindahl Index (domestic and<br>international)|0.21|0.30|0.27|0.42|

_Source:_ AICD database.

_Note:_ ECOWAS = Economic Community of West African States; CEMAC = Economic and Monetary Community of Central Africa; EAC = East African Community; SADC = Southern African Development Community. The SADC aggregates include South Africa and Tanzania and the EAC includes Tanzania, which are not a part of COMESA.

Air transport connectivity in Central Africa has fallen steadily since 2001. This overall pattern is consistent with air transport connectivity in ECCAS and CEMAC, which have both fallen, albeit at different rates (figure 2.12). The air transport connectivity for ECCAS in 2007 was only 40 percent of the connectivity of 2001 and the number of seats in 2007 only a
.64<br>|4.94<br>|5.13<br>|
|Non-poor,Non-poor<br>|84.71<br>|82.06<br>|81.31<br>|72.82<br>(69.77,75.85)|67.02<br>|71.85<br>|70.63<br>|
|R-squared|0.21|0.31|0.43|.|0.21|0.31|0.43|
|Residual Correlation|0.26|0.22|0.22|.|0.26|0.22|0.22|
|Observations|823|823|823|823|823|823|823|
|Data source: CASEN, <br>Note: R-squared is cal|1996-200<br>culated f|6.<br>or oppo|site halv|es of the total 19|96 samp|le. Res|ults are|

<mark>Note: R-squared is calculated for opposite halves of the total 1996 sample. Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Columns [1] and [5] show a simple model with household time invariant characteristics. Columns [2] and [6] add sub-national controls and region fixed effects. Columns [3] and [7] add interactions between household time invariant characteristics and sub-national controls. Results in column [4] show actual panel mobility. 95 percent confidence interval between</mark> <u><mark>parentheses. Upper bound estimations are based on 50 repetitions.</mark></u> 23
Policy Research Working Paper 5912

# **Abstract**

The European Social Survey data are used to analyze informal employment in 30 countries, focusing on employees without contracts and on informal selfemployed workers (who are distinguished from formal workers). Overall the size of informal employment decreases from South to West to East to North. However, working without a contract is more prevalent in Eastern Europe than in the West, except for Ireland, the United Kingdom, and Austria. Between 2004 and 2009, no cases were found when unemployment and dependent informality rates in a country went up together, suggesting that working without a contract is pro-cyclical in Europe. The dependent informality rate is inversely related to skills (measured by either schooling or occupation). Both in Southern and in Western Europe, the highest dependent informality rate is found among immigrants from Central and Eastern Europe and the Former Soviet Union, while in Eastern Europe this group is second after minorities without immigrant background. In the Southern and part of Western Europe, immigrants not covered by European Union free mobility provisions are much more likely to work without a contract than otherwise similar natives. The paper provides evidence that exclusion and discrimination plays an important role in pushing employees into informality, while this seems not to be the case for informal self-employed workers. Both on average and after controlling for a rich set of individual characteristics, informal employees in all parts of Europe are having the largest financial difficulties among all categories of the employed population (yet they fare much better than the unemployed and discouraged), while informal self-employed workers are at least as well off as formal employees. Finally, there is a negative and significant effect of individual-level satisfaction with the national government on the propensity to work without a contract in Eastern Europe, as well as in Western Europe.

This paper is a product of the Human Development Economics Unit, Europe and Central Asia 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
. The results of the maize-specific soil suitability classification exercise are summarized in Table 3. Using the MAPS plot-level soil samples as the basis for classification, 13 percent of plots are considered highly suitable, the majority (75 percent) considered moderately suitable, and the remaining 12 percent of plots considered only marginally suitable. Note that classification into a specific group does not suggest that the plot-level soil properties fit that category in full. Rather, they are most closely aligned with that class relative to the other classes. No plots were classified as not at all suitable, in line with expectations as these are all maize-growing plots.

> 15 Potential output is computed as: observed output/ technical efficiency score.

14
21 Table 4: Household Expenditures Per Person and Social Capital, Comparing the Village and Household Level and Using HRDS data for Incomes.

|Column:|1|2|3|4|
|---|---|---|---|---|
|Source of Data:|SCPS|Human Res|ource and Devel|opment Survey|
|Level of data:|Household|Cluster|Hous|ehold|
|Type of estimation':|OLS|IV|IV<sup>5 </sup>(a)|IV (B)|
|Cluster Level Social Capital|.084<br>1.10|.208<br>2.56|.193<br>2.31|.227<br>1.71|
|Household Level Social Capital'|-.020<br>.526||||
|Household size|-.077<br>7.61|.019<br>1.04|-.080<br>10.5|-.079<br>10.3|
|Average adult schooling<sup>2</sup>|.019<br>1.43|-.057<br>1.42|.021<br>2.87|.021<br>2.79|
|Female head of household (1 =yes)|-.041<br>(.448)|.345<br>1.19|-.009<br>_.150_|-.010<br>.173|
|Asset ownership (In)<sup>2</sup>|.253<br>4.40|.245<br>3.88|.143<br>5.26|.143<br>5.20|
|Self-employed in agriculture (I =yes)<
We exploit other data sources that are available in Nigeria to estimate a long-run poverty trend for the country. First, _sectoral_ real GDP data are available from NBS: this allows the backcasts to be done with more granularity than if only _total_ real GDP growth estimates were available.<sup>6,7</sup> Second, household survey data from the GHS panel are available for four waves: 2010/11, 2012/13, 2015/16 and 2018/19 (see Section 3 for more detail on the GHS data). These data lend themselves to survey-to-survey imputations as they collect household-level information on non-monetary indicators in the same way as in the 2018/19 NLSS. The two surveys not only had identical questionnaires for these non-monetary indicators, but they were also collected by the same team and followed the same methodology as part of an ongoing NBSWorld Bank collaboration, therefore minimizing discrepancies driven by survey methodology and implementation. Moreover, the timing of data collection for the 2018/19 NLSS and 2018/19 GHS overlapped, so it is possible to test whether the imputed consumption and poverty estimates are well aligned with _actual_ consumption and poverty estimates from a similar period.

While the GHS also collects data on household consumption, these data are subject to a series of limitations that do not allow them to be used directly for poverty measurement purposes.<sup>8</sup> In particular, the early rounds of the GHS imposed standard units (such as grams on kilograms) on quantities in the food consumption module when non-standard units may have been more appropriate: this was addressed in later rounds of the GHS. However, this issue does not affect the non-monetary indicators in the GHS, on which survey-to-survey imputation techniques rely.

# 3. Data

The analysis uses four data sources. Household-level data on consumption and non-monetary indicators come from the 2018/19 Nigerian Living Standards Survey (NLSS) and the General Household Survey (GHS) available over four waves in 2010/11, 2012/13,
the publicly available data from the Mexican Institute of Statistics do not allow for more than two cross-tabulations, we use the Census microdata samples from IPUMS (Ruggles et al. 2008) to estimate labor market indicators for more detailed socio-economic groups. For example, when using publicly available Census counts to calculate the number of workers aged 12 years or older by type (wage and non-wage) and sector (private and public), we cannot further disaggregate by age. To test if the inability to restrict the sample to workingage individuals affects the results, we estimate the indicators by age using Census sample microdata (which we can disaggregate in multiple ways), or using a different cross tabulation of the Census counts (for example, by worker type and age) and re-estimate the regressions.

When considering labor market impacts, we focus on wage employment (i.e. paid individuals working for a firm) as this is the category most likely to be affected by automation, as opposed to self-employed, employers or family workers. We also analyze changes in the composition of employment by looking at the share of informal workers in total employment, where informality is defined as being self-employed, unpaid worker or day laborer.

We use data on the number of employees by sector of economic activity and municipality from the 1999 Economic Census to estimate the employment weights _μm,i,_ 1999, which are used to construct the measure of exposure to local automation. These data allow for a higher level of sectoral disaggregation than the 2000 Census of Population of Housing.

**Local labor market (LLM) definition** : Given that we estimate impacts at the LLM level, we group municipalities into functional territories following Berdegu ́e et al. (2017). This methodology allows to increase the sample of LLMs significantly with respect to the official number of 59 Metropolitan Areas defined by the Statistical Institute (INEGI). Using a combination of commuting flows and satellite night light data, the authors group 2,446 municipalities into 1,534
and Firm Growth,” Journal of Finance 53(6), 2107–2137.

- Demirg ̈u ̧c–Kunt, A., and V. Maksimovic, 2002, “Funding Growth in Bank-Based and MarketBased Financial Systems: Evidence from Firm-Level Data,” Journal of Financial Economics 65, 337–363.

- Edmans, A., and C.G. Holderness, 2017, “Blockholders: A Survey of Theory and Evidence,” in B. Hermalin and M. Weisbach (eds): Handbook of the Economics of Corporate Governance, North Holland, Amsterdam, Netherlands, 541-636.

- Falato, A., D. Kadyrzhanova, J. Sim, and R. Steri, 2020, “Rising Intangible Capital, Shrinking Debt Capacity, and the US Corporate Savings Glut,” Journal of Finance, forthcoming.

- Fan, H., E. L.-C. Lai, and Y.A. Li, 2015, “Credit Constraints, Quality, and Export Prices: Theory and Evidence from China,” Journal of Comparative Economics 43, 390–416.

- Edmans, A., and G. Manso, 2011, “Governance Through Trading and Intervention: A Theory of Multiple Blockholders,” Review of Financial Studies 24(7), 2395–2428.

- Fisman, R., and I. Love, 2003, “Trade Credit, Financial Intermediary Development, and Industry Growth,” Journal of Finance 58(1), 353–374.

- Fisman, R., and I. Love, 2007, “Financial Dependence and Growth Revisited,” Journal of the European Economic Association 5(2-3), 470–479.

- Gaulier, G., and S. Zignago, 2010, “BACI: International Trade Database at the Product Level.

39
These response probabilities are then aggregated to the household level (by calculating the mean). Then, using the household-level predicted response probabilities as the ranking variable, all households are ranked into 10 equal groups (deciles). An attrition adjustment factor ( _ac_ ) is then defined as the reciprocal of the mean empirical response rate for the household-level propensity score decile.

Incorporating the attrition correction, the adjusted weights would be:

# _7) Trimming_

Complex weight calculations have the potential to produce outlier weights, which increase the standard errors of estimates. A common practice is therefore to ‘trim’ the weights at this stage to eliminate the outlier weights (see Little et al, 1997). Trimming introduces a small amount of bias into the estimates, but allows estimates to be more efficient. Common values for trimming range between one and five percentage points at the top and bottom of the distribution, and the LSMS-ISA weights are trimmed below the 2<sup>nd</sup> percentile and above the 98<sup>th</sup> percentile. Those weights identified as outliers on the top of the distribution are replaced with the highest value of a weight below the cut-off. For example, with a two-percent trim, weights in the 99<sup>th</sup> and 100<sup>th</sup> percentiles would be replaced with the highest weight

> 9 In some cases, values of round 1 variables may be missing. These values must be imputed using multivariate regression or logistic regression techniques.  There are many suitable imputation command packages available in statistical analysis software, such as the multiple imputation ( _mi_ ) commands in Stata or ( _proc mi_ ) in SAS. Note that the resulting values from these imputations do not necessarily need to become part of the published dataset, and in fact the LSMS-ISA surveys leave missing values as missing to be treated at the discretion of the end user. The imputations are necessary only for the weight calculations so that there are no missing values in the propensity
provisions and procedural aspects for property transfers (table A3). Moreover, while 65% of women know that land acquired by couples married under the common property regime needs to be registered jointly, this is the case for only 46% of men, providing one possible explanation of the fact that the gender benefits from LTR may be slowly eroding (see table 3, panel C).

The survey suggests that, between 2012 and 2015, 1,147 or 12% of parcels owned by households in our sample had been newly acquired. Of these, 51% were registered in the new owner’s name under LTR, 47% had not started the registration process, and 2% went for subsequent registration. Appendix tables 3 and 4 illustrate the procedure employed by providing a side by side comparison of information from LAIS and the household survey regarding area and owners’ names for a given UPI with table 4 including an additional column indicating that the name of the original owner in the 2012 round of our household survey matches

> 13 As is easily verified from table 1, of the 8.55 million parcels in either of these categories, 83% are agricultural, 15% residential, and 2% commercial. From the documentation provided, it seems that in the Biraro et al. (2015) sample proportions are 27%, 51%, and 22%, i.e., commercial and residential land are overrepresented by factors of 3.4 and 11.5 while agricultural land is underrepresented by a factor of 0.33. Even if there were no other issues with their methodology, any estimates arrived at without applying weights will provide a misleading picture of the national situation.

> 14 Note that Rwandan law prevents the subdivision of agricultural land less than 1 ha. Although we do not have information on subdivision of agricultural land in our data, the fact that the majority of parcels transferred being small agricultural parcels (smaller than the average parcel size of the sampled villages) could likely imply illegal
# **4.2 Innovation function: Product, process and organization**

Innovation inputs generate innovation outputs and outcomes. The World Bank Enterprise survey contains information about whether firms implemented technological innovation outcomes (product & process innovations) and non-technological innovation outputs (organizational innovations).<sup>9</sup> For product and process innovation, firms are asked whether they introduced any new or improved product or service, or any innovative new process. For these technological innovation outcomes, there is also information on the degree of innovation in terms of what is defined by “new”; specifically, whether the innovation was new to the firm, local market, national market or international market, respectively.

Organizational innovations are another dimension of innovation that rely less on technological progress but can nonetheless bring about significant improvements in firm performance. We measure organizational innovation through a series of questions that capture whether a firm has undertaken any new organizational method in its business practices, workplace organization or external relations. Specifically, we consider a firm as having introduced an organizational innovation if it engaged in any of the following: 1) outsourced tasks, made changes to external relations, 2) made changes in its management structures, integrated departments/units, or 3) acquired management systems for information and knowledge (proxied by whether it made any changes to its hiring practices of employees or managers). Organizational innovations can improve firm performance such as its productivity by lowering costs, improving the workplace environment, and acquiring knowledge from external sources.

Questions on organizational innovation, however, were only implemented to medium and large size firms, which reduces the estimation sample by more than half. Therefore, our preferred model includes only product and process innovations.

Specifically, the three innovation outputs can be defined as:

|PDit= β3’X3it+ ε3it|(2)|
|---|---|
|PCSit= β4’X4it+ ε4it|(3)|
|ORGit= β5’X5it+ ε5it|