rafmacalaba/data-use-mentions-extended huggingface dataset:
Finally, we conclude the analysis by attempting to isolate the impact of the _Canon_ from the direct effect of the mining activity. For that purpose, we run the following specification: where, Canonpd represents the government transfers related to mining revenue received by each district during 2002-2006. Because the Canon’s attribution rule does factor in socioeconomic indicators for the provincial and departmental allocations, we instrument the amount of Canon received by each district with three variables: the log of (one plus) the value of the mineral extracted in each district, province and department between 2002 and 2006.

# **IV. Results**

We begin the analysis by considering the possibility of preexisting differences between producing and non-producing districts. In particular, we test whether socioeconomic outcomes in 1993 differ between districts where a mine was active during 2002-2006 and districts with no mining activity. The socioeconomic outcomes under consideration are the illiteracy rate and the index of basic necessities uncovered, the two variables for which we have information for 1993.

Results are shown in Table 3. The results corresponding to each outcome variable are organized by rows, with columns assigned to different regression specifications and comparison groups. The comparison groups are defined according to whether or not we include in the regressions department and province dummies. In regression (1), the comparison group consists of all non-producing districts in non-producing provinces. In regression (2), the comparison group is restricted to non-producing districts in non-producing provinces within the same producing department. Finally, in regression (3), we compare producing and non-producing districts in the same province. In all cases, we control for timeinvariant district characteristics (provincial capital, log of area, and log of altitude), and district characteristics obtained from the 1993 Census (log of total population, share of rural population, and share of households without electricity).<sup>7</sup> Results are encouraging. While there are differences in the

> 7 Observe that, when we look at 1993 outcomes, the analysis may
the measure of revenue and output. Measuring gross output relies on sales and inventory data, both prone to statistical errors. Moreover, price data are usually not available for different products at the firm, so that inferring quantities from revenues may be inaccurate as products and their quality change.

Roughly speaking, there are two main sources of productivity growth. The first source is withinfirm productivity growth; that is, firms becoming more productive thanks to better technology or management practices. In this case, policy prescriptions to increase productivity are geared towards increasing technological diffusion and innovation, learning and skill upgrading, and infrastructure provision. This conventional understanding of productivity growth has been the subject of a long-standing literature and dominated much of policy discussions until the end of the twentieth century. The second source consists of improving allocative efficiency, that is, the reallocation of resources towards more productive uses. This can imply the transfer of capital and labor across firms and across sectors, as well as the entry and exit of firms. This second source of productivity growth has been emphasized in the literature since the turn of the century (see, for example, Banerjee and Duflo, 2005 and Restuccia and Rogerson, 2008). Policies associated with allocative efficiency emphasize correcting or reducing market distortions. In this paper, we focus on the second source of productivity improvement, concentrating our analysis on resource allocation across firms within a narrowly defined industry.

What is resource misallocation in a narrowly defined industry? It is misallocation of production inputs across firms that produce similar products and compete with each other, while retaining some degree of control over their prices. In an economy with low levels of distortion, more 2
|-0.125***|0|
|Not employed in the<br>agricultural sector|-0.012***|-0.016***|0.0057|-0.034***|-0.039***|0.0057|
|Access to electricity|-0.041***|-0.016***|0.0084|-0.094***|-0.069***|0.0084|
|Piped drinking water|-0.005**|-0.003***|0.4166|-0.011***|-0.009***|0.4166|
|Improved sanitation|-0.021***|-0.025***|0.1131|-0.063***|-0.067***|0.1131|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

**Table A16: Identifying the $3.20 poor based on covariates of poverty** _Indonesia 2017_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(
|Rainfall stress|Number of 20-day periods with < 40mm of|0.29|-0.90|1.60|6531|
|---|---|---|---|---|---|
||rain|||||
|_Other independent_<br>|_variables_<br>|||||
|Used CSA|Used min till, hybrid maize seed or fertilizer<br>(yes = 1)|0.82|0.00|1.00|6531|
|Accessed credit<br>(yes =1)|Accessed credit from any source|0.18|0.00|1.00|6531|
|Crop diversity|If Simpsons crop diversity index > median<br>(yes =1)|0.45|0.00|1.00|6529|
|Asset index|Asset index from PCA|0.10|-2.56|23.91|6524|
|femalehead|Female head of household (yes = 1)|0.23|0.00|1.00|6531|
|ageh|Age of household head (years)|52.01|19.00|105.00|6531|
|eduhead|Education of household head (years)|6.04|0.00|18.00|6531|
|dep_rat|(Number of members aged < 14 + =>65)/#<br>aged 15 – 64|162.46|0.00|1300.00|6319|
|mem0_5|# of members 0 – 5 years old|0.51|0.00|7.00|6531|
|mem5_10|# of members 5 – 10 years old|0.96|0.00|7.00|6531|
|mem10_15|# of members 10 – 15 years old|1
*|13.82|0.27|47.13|5.88|6.50|0.06|15.23|7.12|3.98|
|**2000**|12.85|1.02|39.97|4.29|10.41|0.86|10.66|15.97|3.98|

_Source:_ Authors‘ calculations based on data described in text.

R&R: Raw and Remainder combined.

39
be seen from columns 4-6, the impact on both outcome variables is substantially more pronounced once we control for SGRY. Comparing column 6 with column 3, not controlling for SGRY underestimates (in absolute terms) NREGA’s impact on public employment by approximately 25% and private employment by 34% in the dry season.

> 18 Note, the estimates obtained from AWI and NSS data are different and they need not be the same. First, the NSS data is of lower frequency relative to AWI data. Small annual changes in wages may compound when the treatment and control districts are compared over a few years. Second, the AWI data reports agricultural wages, while the NSS data provides information on income from casual work. The two measures, while correlated, are not necessarily the same.
Policy Research Working Paper 10080

# **Abstract**

This paper presents evidence on the extent to which a set of real-time indicators tracked changes in gross domestic product across 142 countries in 2020. The real-time indicators include Google mobility, Google search trends, food price information, nitrogen dioxide, and nighttime lights. Google mobility and staple food prices both declined sharply in March and April, followed by a rapid recovery that returned to baseline levels by July and August. Mobility and staple food prices fell less in low-income countries. Nitrogen dioxide levels show a similar pattern, with a steep fall and rapid recovery in high-income and upper-middle-income countries but not in low-income and lower-middle-income countries. In April and May, Google search terms reflecting economic distress and religiosity spiked in some regions but not others. Data on nighttime lights show no clear drop in March outside East Asia. Linear models selected using the Least Absolute Shrinkage and Selection Operator explain about a third of the variation in annual gross domestic product growth rates across 72 countries. In a smaller subset of higher income countries, real-time indicators explain about 40 percent of the variation in quarterly gross domestic product growth. Overall, mobility and food price data, as well as pollution data in more developed countries, appeared to be best at capturing the widespread economic disruption experienced during the summer of 2020. The results indicate that these real-time indicators can track a substantial percentage of both annual and quarterly changes in gross domestic product.

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 dnewhouse@worldbank.org and upape@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
different years and using different questionnaires to collect consumption expenditures. A direct comparison is only possible in 2004/05 (Figure 1 and Figure 2). For that year, the CES reports average consumption expenditures approximately 10 percent higher in rural areas, and 5 percent higher in urban areas, than in SCS. Average consumption does not capture all differences between the two surveys. Comparing the CES and SCS Health in 2004/05, shows that the consumption distributions in the two surveys are very close, with the SCS Health stochastically dominating the CES in the bottom of the distribution but the opposite is true above around $100 per month in 2011 PPP terms (Figure 3, top panels). The comparison of these surveys indicates that the consumption aggregate included in the CES surveys is systematically different than the consumption aggregate captured by the SCS Health and Education surveys. Hence, measures of poverty using the consumption aggregate from the SCS surveys cannot be compared to those using CES surveys.

On the other hand, SCS Health and Education surveys show higher average consumption in year 2017/18 than in previous vintages of the same survey (that is, years 2014 and 2007/08 for the Education survey, and 2014 and 2004/05 for the case of the Health survey). Going beyond the simple averages, a stochastic dominance analysis shows that the distribution of household consumption expenditures of the SCS Health 2017/18 survey is to the right of the distribution of the 2004/05 health survey (Figure 3, middle panels) and the same with respect to the 2014/15 health survey (Figure 3, bottom panels), for any poverty line below $300 per month. This could indicate that household consumption has increased for all those households at the bottom of the distribution and hence poverty is lower in 2017/18 than in previous years.

# **2.2. Non-official data sources**

The subsample of the IHDS survey that was collected in 2017 for three states (Bihar, Rajasthan and Uttarakhand)
# **Appendix B: Summary of Village and Village Cluster Projects**

**Table A1** and **Table A2** give an overview of all the village and village cluster level projects implemented as part of the Tuungane program, respectively. Information is based on 2012 tracking data from the implementing partner.

# **Table A1. Village Level Projects**

|**Project type **|**#**|**%**|
|---|---|---|
|Bridge|86|4%|
|Classroom|872|37%|
|Common room|67|3%|
|Health facility|176|8%|
|Health facility equipment|81|3%|
|Latrines|13|1%|
|Market|41|2%|
|Mill|139|6%|
|Mosquito nets|20|1%|
|Other construction|8|0%|
|Other purchase|30|1%|
|Purchase agricultural tools|54|2%|
|Purchase animals|33|1%|
|Purchase doors, windows, benches, etc|76|3%|
|Purchase seeds|13|1%|
|Route|77|3%|
|School material|215|9%|
|Water source|334|14%|
|Total|2,335|100%|

Note: “Other construction” includes a bus stop, electricity and a morgue. “Other purchases” include projects like an oil press, community radio, satellite dish, field for pygmies, sewing machine and brick press.

3
# Appedix 1: Data sources and description

The data used in this study are compiled from (1) DHS Final Reports (2) NFHS Final Reports and (3) Reconstructed gender arLd provincial disaggregated outcomes for Pakistan. Except for a very few exceptions, the transfonnati ons required to go from the published data to that used here involve no more than taking the ratio of the female to the male value. The exceptions are discussed below.

# _Baluchistan province of Pakistan_

Because of the limited data from the Baluchistan province of Pakistan, the data were adjusted as follows. The female/male child mortality ratio (4q1) is not that derived from the DHS but is calculated from the Pakistan Integrated Household Survey (PIHS) which was carried out in 1991. The ratio as calculated from the DHS data is 8.92 (4q1 for males is 6.4, for females it is 57.1, which seems implausible). The ratio calculated from the PIHS is equal to 1.79. In the calculation of consultation and no treatment the ratios are again implausible when including all children who suffered from diarrhea, ARI, and fever. These ratio are replaced by the ratio including only those who had a sample weight of less than 1. The corresponding changes in the data are as follow:

## Changes made to Baluchistan female / male ratios for 6 variables

|Description|Name|Raw ratio|Ratio using only<br>those observations<br>with weight less<br>than I|
|---|---|---|---|
|Female relative to male: percent with ARI who were<br>taken for consultation (usually includes hospital, health<br>center, clinic, doctor, or other health professional)|aritd|.713|1.00|
|Female relative to male: percent wilh fever who were<br>taken for consultation|fevtd|.519|.984|
|Female relative to male
To address misspelling, we take advantage of the recurrent collection of names during the experiment. Students or their parents provided student names on nine occasions. We consider all different spellings for each name as possibly right in the matching exercise and compare all of them with the SRF registry.

In addition to the CPF, we extract from the SRF registry the name, date of birth, gender, and municipality (referring to the last update) of every individual born from 1988 to 1999. We match this data to the list of student names for all students, including those for whom we had valid CPFs from the short-term impact evaluation data.

After matching by name, we drop any matches that are not age compatible to reduce the incidence of homonyms or individuals whose name matched misspelled student names. We use the dates of birth from the SRF to compute the students’ age in August 2010, when the data on age was collected during the baseline survey for the short-term impact evaluation. We then compare this calculated age with the answers given in the August 2010 survey. The possible answers to that survey were: (i) 13 years old or younger, (ii) 14 years old, (iii) 15 years old, (iv) 16 years old, (v) 17 years old, and (vi) 18 years old or older. If students selected “13 years old or younger,” we considered them to be a match if their SRF computed age was between 10 and 13 years old. If students selected “18 years old or older”, we considered them a match if their SRF computed age was between 18 and 22 years old. A big caveat here is that not all students in the short-term impact evaluation took the baseline survey (i.e., the students who joined the classes in the study after the

> 4 The Cadastro Único registry for December 2019, which includes poorer families representing around a third of the Brazilian population, lists a CPF for 96.2 percent of
was driven by factors external to Mexico, the credit risk of Mexican firms operating in energyrelated sectors ramped up as a result.<sup>3</sup> Importantly, the degree of Mexican banks’ exposure to the energy sector varied substantially before the shock. We exploit this cross-bank variation to identify how banks reallocated their credit depending on their ex-ante exposure to the struggling sector. We show that banks with large exposures to the energy sector had an incentive to maintain borrowers afloat even as their creditworthiness deteriorated, causing a decline in lending to other sectors (Caballero, Hoshi, and Kashyap 2008; Peek and Rosengren 2005).

How banks cope with shocks and transmit them to other sectors of the economy remains an important issue in finance. However, there are several challenges to overcome to answer these questions rigorously. One challenge is having a credible counterfactual, since aggregate shocks may affect the entire banking sector simultaneously. To overcome this hurdle, we exploit the late 2014 oil price shock and adopt a difference-in-differences approach with ex-ante similar banks differing in their exposure to energy-related sectors prior to the shock. We define ex-ante bank exposure as the ratio of loans to firms in energy-related sectors over the bank’s tier 1 capital in the month prior to the unanticipated shock.<sup>4</sup> A second challenge in identifying banks’ strategies is to isolate changes in the supply of credit from changes in the demand for credit, as aggregate shocks might impact firms’ credit needs. To control for time-varying credit demand, we rely on loan-level data obtained from the Mexican credit registry on the universe of commercial bank loans from January 2013 to June 2016. Loanlevel data allow us to saturate our specifications with bank*firm and firm*month fixed effects, exploiting variation in the credit conditions of a firm-bank pair over time, as well as by the same firm across different banks with varying exposures to the shock (Khwaja and Mian 2008; Morais et al. 2019).

> 3 The global energy price drop was
Estimates of the standard errors of the fixed effects _σ_ <u>(</u> _<u>wd,</u>_ 09) and _σ_ <u>(</u> _<u>wd,</u>_ 04), which I use below to construct regression weights, are obtained following the procedure of Haisken-DeNew and Schmidt (1997).

# **3.4**

Rewriting equation (7) to reflect the price data discussed above, I wish to estimate where _STATEd_ denotes the state in which district _d_ is located. There are two endogeneity issues to contend with: measurement error and simultaneity between wage and price changes.

As to the first issue, both the crop value shares, _sd,j_ , and the crop-specific log-price changes, ∆ _pSTATEd,j,_ may be measured with error. Putting aside the latter concern momentarily and assuming that measurement error is confined solely to value shares, I could deploy the instrument where _ad,j_ is the area share of crop _j_ in district _d_ . To be sure, cropped areas may also be measured with error, but these errors should not be correlated with those of crop production and prices.

Clearly, _IV_ 1 _d_ does not deal with measurement error in price changes, which could arise if, e.g., the marketed varieties or grades of a certain crop in a certain state change over time. Another concern is unobserved district-level shocks (or trends) correlated with both wage and price changes. For instance, suppose that a particular district has been industrializing relatively rapidly over the 2004-09 period, or that it has experienced comparatively rapid technological improvement in agriculture. Both types of shocks would tend to raise district 14
of the ELMPS is the panel component, which allows for a better modeling of transitions into different labor market states, as it follows the same workers across time.<sup>8</sup> Table 1.a: Labor states in FY18 (males)

||%|Obs.|
|---|---|---|
|Agriculture|13.2|4,764|
|Industry - Informal|14.3|5,161|
|Industry - Formal|5.8|2,081|
|Services - Informal|16.6|6,002|
|Services - Formal|18.0|6,515|
|Not working|32.1|11,603|
|N||36,126|

Table 1.b: Labor states in FY18 (females)

||%|Obs.|
|---|---|---|
|Informal|10|3,524|
|Formal|8|2,944|
|Not working|82|29,282|
|N||35,750|

Source : Authors’ calculations using HIECS 2017/18. Note : Unweighted percentages.

Using the panel data in ELMPS, a Mincer type multinomial logit regression for adults aged 15 and older was estimated to create a propensity score with the likelihood that an individual with a given set of observable characteristics (e.g., age, education, household size and demographics, location) would stay or transition to a different labor market state (Annex B). This propensity is then calculated for individuals in HIECS and used to remap them to match counterfactual FY20 states inferred from the LFS. This analysis was done separately by gender. Table 2 presents the two-year transition matrix used in the model, building on the LFS trends and ELMPS transition probabilities by gender to update the HIECS. About 18 percent of the sample of working-age males and 8 percent of the sample
nature of the data, (ii) the scale, multidimensionality and granularity of the information collected; and (iii) its ability to capture actual human behavior recorded in real-world transactions.

The emergence of big data has also enabled researchers to examine aspects of the labor market that have historically been difficult to analyze (Horton and Tambe, 2015). For instance, using data from the predominately US-focused online job portal “Career Builder,” Marinescu and Rathelot (2015) assessed the effects of geography on worker application patterns. Their research concluded that while job seekers typically do not want to apply for jobs far from where they live, the effects of geography are too small to explain much of the frictional unemployment in the labor market. This type of analysis would have been impossible without big data analytics. Other studies using data from job portals have been conducted in the US, Europe and middle-income countries like China and the Slovak Republic. Moreover, social networks such as LinkedIn, Facebook and Twitter, employment-information websites such as Glassdoor, and trendinformation providers like Google Trends, are also potential all new data sources for labor market analysis (Lenaerts, Bevlavy and Fabo, 2016).

The establishment and rapid expansion of online job platforms have dramatically reduced the cost of information access for both job seekers and employers. Kroft and Pope (2014) and Mang (2012) argue that there has been a large-scale shift from posting job listings in newspapers and other print media to websites and that this process has lowered the cost of acquiring employment information. Moreover, several studies from developed countries have found that the use of employment websites has reduced unemployment rates (Beard et al., 2012; Kuhn and Mansour, 2014), though other studies do not find strong evidence of an impact on wages (Kuhn and Mansour, 2014; Shahiri and Osman, 2014). However, as Shahiri and Osman (2014) point out, many people in developing countries—including both employers and job seekers—cannot take advantage of employment websites due
each firm point in the UBI dataset is overlaid by the 1km grid square level data on population, employment, and Landscan. Each firm is then assigned own-square measure

> 18Business establishments were first stratified by industry sector and within each industry sector, business establishments were further stratified by employment size as per the following categories of employment size 1, 2-4, 5-9, 10-19, 20-49, 50-99, 100-499 and 500 or more employees; and further by turnover; thus less than 5 million shillings, between 5 and 10 million shillings and more than 10 million shillings. Given the significant contribution of the larger establishments, from previous surveys, to the value of production; all the establishments with 50 or more employees were supposed to be sampled with certainty, (a probability of 1), while those employing fewer than 50 employees were subjected to probabilistic sampling.

> 19An issue is that if we look in the 2002 census data, matching it alongside the firm locations from our 2002 survey, we see that for over half of our firms we cannot find other firms from the census that are in the same industry and within 150 meters of the survey firms, indicating either that there is a lot of firm turnover or issues in recording locations.

28