rafmacalaba/data-use-mentions-extended huggingface dataset:
, 2010, _125_ (4), 1577–1625.

- **, , and Prem Sangraula** , “PovcalNet,” _Available at_ _`http: // iresearch. worldbank. org/ PovcalNet/ home. aspx` ._

- **Chen, Xi and William D. Nordhaus** , “Using Luminosity Data as a Proxy for Economic Statistics,” _Proceedings of the National Academy of Sciences_ , 2011, _108_ (21), 8589–8594.

- **Chetty, Raj, John N Friedman, Nathaniel Hendren, Michael Stepner, and The Opportunity Insights Team** , _The economic impacts of COVID-19: Evidence from a new public database built using private sector data_ number w27431, National Bureau of Economic Research, 2020.

- **Chodorow-Reich, Gabriel, Gita Gopinath, Prachi Mishra, and Abhinav Narayanan** , “Cash and the economy: Evidence from india’s demonetization,” _The Quarterly Journal of Economics_ , 2020, _135_ (1), 57–103.

- **Clark, Hunter, Maxim Pinkovskiy, and Xavier Sala i Martin** , “China’s GDP growth may be understated,” _China Economic Review_ , 2020, _62_ (C).

- **Cragg, John G and Stephen G Donald** , “Testing identifiability and specification in instrumental variable models,” _Econometric Theory_ , 1993, pp. 222–240.

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/good index k and the sector j), relative to some numeraire. Then:

# pi = μi + mci

where μi ≈ μ ̄i − Γ [pi − p] is the log markup, which I assume is related to the relative price pi − p, with reduced form elasticity Γ ≥ 0 (which might depend on country-, sector- or firm-specific characteristics). That is, the higher is the price of good k from country i relative to the sectoral/aggregate price index p, the lower the desired markup. In the AB model (in Section 4), higher prices relative to the sectoral/aggregate price reduce market shares which reduce desired markups, though the microfoundation varies across models (I ̄ assume the firm’s direct contribution to p here is small). Let the log marginal cost be mci = mci +(1 − φus − φc)(wi − ai) + φusvus + φcvc which depends on unit labor costs (wi − ai) in the country of production with weight 1 − φus − φc and the price of imported intermediate inputs from the US (vus) or the third country (vc). For simplicity, assume intermediate inputs produced in c or the US and sold in i are produced by competitive firms using only labor with constant returns to scale such that vc = wc − ac and vus = wus − aus. Substituting and suppressing constants, the expression becomes: Adding Γpi to both sides, rearranging, and subtracting log US unit labor costs (ULC, wus − aus) from both sides yields the pricing Equation 2.

Where reri = wi − ai − [wus − aus] is the log real bilateral ULC-based real exchange rate, and pRi, pR and vRc are respectively the log real import price of the firm, the sectoral/aggregate price and imported intermediate price (from c), relative to US ULC. Define rerc = wc − ac − [wus − aus] as the log bilateral real
jective measure is based on the self-reported difficulty of financing for the operation and growth of the business. In 2002 and 2005 respondents are separately asked to assess the degree of difficulty in access to (“e.g., collateral required or financing not available from banks”) and cost of financing (“e.g., interest rates and charges”), ranging from 1 (“No obstacle”) to 4 (“Major obstacle”). In 2009 and 2013, these two questions are merged into one and respondents are asked about the concerns due to access to financing. Also the scale of the importance of the constraint is different and varies from 1 (“No obstacle”) to 5 (“Very severe obstacle”). We harmonize the scales across years and construct the variable as follows: The subjective and objective measures of being capital constrained are significantly correlated. This evidence is consistent with Hallward-Driemeier and Aterido (2009) and Gorodnichenko and Schnitzer (2010) that relate subjective financing constraints to the amount of credit issued to the private sector, expressed as a percentage of GDP.

However, the objective measure of credit constraint may suffer from endogeneity bias. Firms may be credit unconstrained because banks extend them credit (supply-side channel) or because they do not demand credit as demand falls (demand-side channel). To disentangle these two channels we propose two instruments for the objective measure: the share of domestic credit provided by the banking sector (as a percentage of GDP), and the share of foreign banks out of the total number of banks in each country at time _t_ and _t −_ 1. Both instruments capture the health of the financial market and are correlated to whether or not a firm may receive credit but exogenous to the firm’s export decisions.

Credit constraints may have an important impact on firm exports in ECA countries, especially given the large presence of foreign banks in the region. Based on data from Claessens and van Horen (2012), Figure 2 shows
a not dissimilar approach has been used with some success for inferring attributes of preferences, such as risk aversion or valuations of environmental factors.

# **6. Conclusions**

Subjective data offer to expand the information set traditionally used for assessing welfare and measuring poverty. Self-assessed welfare is not equated with welfare. Rather, the promise of subjective data is to help identify the weights on the dimensions of welfare for which prices are missing (or unreliable) and in determining the social subjective poverty line—below which people tend to think they are poor, but above which they do not.

28
) were almost 11 percentage points more likely to lose their jobs during the pandemic than men with similar characteristics including similar jobs in terms of WFH. However, if we compare similar women and men in jobs fully compatible with remote work, women were only 2.8 percentage points more likely than men to lose their jobs during the pandemic. This result is in line with our previous discussion: the

> 11Our preferred specification includes this adjusted index that captures at the individual level the combined effect of both the characteristics of the occupation and the availability of technology at home on the ability to do teleworking. We also estimate models including separately the unadjusted WFH index and household internet access. All our main results hold.

> 12Unfortunately, the HFPS does not have information on hours worked. However, from other sources (e.g., SEDLAC) we know that self-employment exhibits a greater dispersion of weekly working hours than salaried work.

> 13Tables A.1 and A.2 show the results of estimating other more parsimonious specifications that gradually add controls. Models in Table A.1 explore conditional gender differences in the probability of experiencing job losses. The results indicate that the probability of job losses is 7.7 percentage points higher for women than for men with similar characteristics—i.e., same age, education, country, etc. Models in Table A.2 add the WFH index as a regressor. Given everything else, workers in jobs fully compatible with remote work (WFH = 1) are 8.5 percentage points less likely to have lost their jobs during the pandemic than workers in jobs incompatible with remote work (WFH = 0).

11
Laboral y de Corresponsabilidad Social – Labor and social co-responsibility Survey) 2012 and find that 79 and 71 percent of women with high and low qualifications, respectively, reported that their children (zero to five years old) are not in daycare because someone else takes care of them. The second most frequent response, however, is that the father prefers the mother to take care of the child (10 percent of women with low qualifications and 8 percent of highly qualified women).<sup>6</sup> Indeed, data from the World Values Survey indicates that a significant share of the population believes that “when a woman works for pay, children suffer”: 32.6 percent agreed and 10.4 percent strongly agreed (2010-2014 wave). Importantly though, those beliefs are changing: The share of respondents agreeing and strongly agreeing was significantly higher in the 1990 WVS wave (50 percent and 25.9 percent respectively).

<mark>This paper presents the results of a qualitative study aimed at deepening the understanding of factors that determine the demand for childcare in Mexico City. The study aims to shed light on what is driving the decisions of Mexican mothers (and fathers) to send their children to childcare or not, with the ultimate objective of adequately informing policies aimed at addressing the existing gaps and imbalances. The paper is divided as follows: After this introduction, the methodological approach is presented. This is followed by the main results of this study, disentangling the decision process for accessing childcare services. This section ends with a discussion about differences in women's decisions to access childcare centers when comparing different social groups. The last section presents the main conclusions related to the complex decisions that underlie the demand for childcare services in Mexico City.</mark>

# Methodology

<mark>The objective of this study is to deepen the understanding of factors that influence the demand for childcare services in Mexico using qualitative research. The study aims to better understand how women and men decide to
All districts in mining regions have limited ability to raise their own taxes. Thus, their investments in infrastructure and poverty alleviation programs depend on revenues transferred from the central government. The main ones are the _Fondo de Compensación Municipal (FONCOMUN)_ , and, since 2002, the _Canon Minero_ . While the allocation rule of _FONCOMUN_ favors remote and poor districts, the _Canon_ is mainly allocated on the basis of mining production value. The _Canon’_ s rule is as follows: 50 percent of mining tax revenues are distributed back to subnational governments; of this amount, 10 percent goes directly to the corresponding producing district; 25 percent is distributed among all districts in a producing province; 40 percent is distributed among all districts in a producing department; and the remaining 25 percent is transferred to departmental governments and universities.<sup>4</sup> Apart from the 10 percent transferred directly to producing districts, the allocation of the Canon across all (producing and non-producing districts) also depends on district characteristics that include socioeconomic conditions.

Before the implementation of the _Canon_ , _FONCOMUN_ represented the main sources of revenues for most districts. However, as a result of Peru’s mining boom, the _Canon_ has grown substantially, particularly for districts in producing regions. In 2007, the year of our analysis, _FONCOMUN_ represented 32.9 percent of districts’ budget and the _Canon_ 21.4 percent (excluding the Department of Lima). The correlation between the amount of FONCOMUN received from the start of decentralization until the years of our analysis (2002-2006), and being a mining district, is statistically insignificant, hence there may be little crowding out of other government transfers from the _Canon_ . Accordingly, we shall find that controlling for additional transfers from the central government does not affect the results.

# **III. Data and Methodology**

Our outcome variables are a set of district-level living standards measurements for 2007: average per capita expenditure, the poverty and extreme poverty headcount indexes, a measure of uncovered basic
low level of law enforcement is expected given the high level of informality, implies the results are a lower bound effect. Second, given the extensive geographical coverage of the data, the paper highlights the need to account for geographical heterogeneity within an economy – areas beyond the capital city may face a different legal environment due to lack of enforcement, or face more ingrained social norms that are unresponsive to legal reforms. Finally, it is one of the few papers that explore the effects of employers’ discriminatory behavior on female labor market outcomes using nationally representative firm-level data in a developing country context. Within this strand of literature, it complements a recent study by Kotikula et al. (2020) that explores discriminatory firms, defined in terms of their perceptions of women employees, in South Asia.<sup>6</sup> The rest of the paper is organized as follows. Section 2 gives some background about the DRC in general and the DRC labor market in particular; this section also discusses the institutional and cultural environment in the country. Section 3 describes the data used in the analysis. Section 4 presents some patterns of

> 6 See Azmat and Petrongolo (2014) for a review of discrimination studies looking at gender and labor market outcomes.

4
by treatment status.<sup>11</sup><sup>_,_12</sup> Importantly, the SBM program was similarly active in both our study arms throughout the study period. This is confirmed by SBM officials’ reports on sanitation activities that took place in their communities between 2015-2017, shown in Table 1. The table compares reports between control and treatment villages, showing statistical balance across the board. The vast majority of SBM officials report that sanitation activities had taken place (79% of all officials), and out of those that report to have hosted such activities nearly all report SBM activities (76% of all officials). On average, SBM officials report that these activities were mainly carried out by the GP Sarpanch (i.e. the village leader). Community sanitation activities aimed primarily to create awareness about sanitation, with street plays and village meetings particularly popular. 40% of the village officials also reported that wall paintings or wall banners have been installed to promote improved sanitation practice, and most of these installations were also observed by the interviewer during the endline visit. Other than awareness creation activities and subsidy schemes, very few SBM officials report any other sanitation activities organised at the village level.

The experimental variation and the almost simultaneous introduction of the sanitation loans and the SBM subsidy allows us to gain an understanding whether and how micro-credit was successful in complementing the SBM subsidy program.

# **3.2 Data**

## **3.2.1 Data sources**

To assess the extent to which a micro-credit program can support the SBM subsidy program, we link together three different data sources which each contain unique information needed in our analysis: (i) SBM administrative data on household toilet ownership, subsidy receipts and official VG classification status (henceforth referred to as ‘SBM data’); (ii) household survey data on toilet ownership, open defecation practice and various household socio-economic characteristics as well as borrowing data that were collected for the
# **3 Methodology**

## **3.1 Research Design**

Our impact evaluation focuses on 36 urban public works sites in 17 communes of the PEJDC intervention area in 9 regions of Burkina Faso (Boucle du Mouhoun, Centre, Centre-East, CentreNorth, Centre-West, Centre-South, Hauts Bassins, North, Plateau Central). The 36 sites were selected based on their potential to support a childcare center. The number of participants at each site ranged from 100 to 500 and the proportion of women ranged from to 58% to 96%.

We used a computer program to randomly assign the 36 urban public works sites in the research sample to a treatment group (18 sites) with implementation of community-based childcare centers and a control group (18 sites) with no childcare centers. To increase the similarity of the 2 groups, the randomization was stratified across 10 blocks based on the geographical location and number of brigadiers assigned to each site. The sites were defined by _communes_ (municipalities) in smaller urban areas and by _arrondissements_ (neighborhoods) in the two larger cities of Ouagadougou and Bobo-Dioulasso. Figure 1 illustrates the spatial distribution of our study sites.

## **3.2 Sampling**

Our sample comes from the register of public works participants in the 36 study sites. We began with a screening survey to restrict our sampling frame to women who were primary caregivers for a child aged 0 to 6 (because the centers were open to children below the age of 7). Among participants meeting these eligibility criteria, we randomly selected 2,160 individuals. In each of the 36 sites, the size of the sample was proportional to the number of eligible participants in that site. The study sample therefore included 2,160 households, with data collected on the 2,160 female public works participants, their spouses or partners if co-residing, and any children aged 0-6 in their care, for a total of up to 4,320 individual interviews and a minimum of 2,160 child
predominantly used for productivity purposes and tasks related to work and education.

Third, generative AI tools have achieved unprecedented global diffusion both in terms of scale and speed, though low-income countries are falling further behind middle-income countries. ChatGPT is used in 209 of 218 economies worldwide. The United States accounted for over 70% of total traffic for ChatGPT upon its launch, but that share dropped to 25% within one month. Traffic originating from middle-income economies surpassed that of high-income economies within six months, and middle-income economies now contribute more than half of the traffic. In addition to the US, India, Brazil, the Philippines, and Indonesia round out the top five economies in terms of ChatGPT traffic. The over-representation of middle-income economies is remarkable when put into perspective: for example, India only accounts for 14% of US GDP, 35% of US electricity consumption, but over 50% of US ChatGPT traffic. Similar patterns are observed across many other countries, attesting to the widespread interest and uptake of generative AI. Still, low-income economies only represent less than 1% of ChatGPT traffic.

Forth, panel data regressions reveal that countries’ income level, share of youth population, the availability and quality of digital infrastructure, and human capital are strongly correlated with higher uptake of generative AI. Specialization in the digital sector and white-collar occupations, and English fluency predict higher chatbot usage but not other types of generative AI usage. Higher internet speed is more important for image and video generation tools than chatbots. Youth population, better digital infrastructure, higher literacy rate and English fluency also predict more intensive usage of ChatGPT, measured with average duration per session.

Finally, we found indicative evidence that generative AI tools are disrupting the current traffic patterns for certain websites and altering individuals’ online activities. Information websites like Wikipedia, translation and writing tools like Grammarly and Google Translate, IT professionals’ community Stack Overflow have experienced substantial drops in traffic immediately after the launch of GPT-4. Traffic to some of these sites have recovered as they integrated
to 40 percent increases the likelihood of conflict by half (from 1 to 1.5 percent). However, the likelihood of conflict starts decreasing with further decentralization, and becomes insignificant at about 50 percent. In other words while small transfers are correlated with an increase the probability of conflict, large transfers are correlated with a decrease in it. While these results apply to all countries, such a humpshaped relationship becomes more prominent when we focus on the oil-rich country group only (oil revenues above 1 percent of GDP). However, with a substantially reduced sample size, we also lose significance. The remaining of the paper will build upon such a stylized fact.

> 9See also Barkey (2009) and Voller (2013).

> 10Immediately after the events in Tunisia and Egypt, King Abdullah of the Kingdom of Saudi Arabia announced large-scale benefits especially for low and middle income citizens. These included salary increases, bonuses for public employees, and construction of low-income housing units. The strategy of buying off social peace was effective in minimizing the protests, and any remaining unrest was then cracked down easily. Other Gulf monarchies followed similar approaches. Kuwait increased public salaries by 115 percent while the Gulf Cooperation Council promised an additional $20 billion fund for investments in Bahrain and Oman.

> 11The selection of data reflects the availability of government expenditures and decentralization figures, which we use as a proxy for transfers in the absence of a good measure of natural resource transfers. More specifically, we use expenditure decentralization measured as the share of subnational governments in general government expenditures. Our measure of conflict is conflict onset from the UCDP/PRIO Armed Conflict Database. A complete description of the data is provided in Appendix 2.

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|S/N|Table A3: ODIN d<br>Data dimension|ata dimension,catego<br>Data category|ryand indicators<br>Representative indicators|Categorical disaggregation|Coverage element 1 scoring guidelines*|
|---|---|---|---|---|---|
|1|Social Statistics|Population and Vital<br>Statistics|(1.1) Population by 5-year age<br>groups<br>(1.2) Birth rate<br>(1.3) Death rate|(1.1) Sex; marital status<br>(1.2) Sex; marital status<br>(1.3) Sex|To receive full point: Must have all indicators disaggregated<br>by sex.<br>To receive half point: Must have at (1.1) with one<br>disaggregation, or (1.2) and (1.3) with one disaggregation<br>each.|
|2||Education Facilities|(2.1) Number of schools or<br>classrooms;<br>(2.2) Number of teaching<br>staff;<br>(2.3) Education budget data.|(2.1) School stage; school type.<br>(2.2) School stage; school type;<br>(2.3) School stage; functional<br>categories.|To receive full point:Must have all three indicators<br>disaggregated as follows: (2.1) and (2.2) by school stage (see<br>notes) and one other disaggregation and (2.3) with one<br>disaggregation.<br>To receive half point:Must have one indicator with two<br>disaggregation or two indicators with one disaggregation<br>each
, the extent of poverty reduction during 2015-2019 is estimated to be notably lower than earlier projections based on growth in private final consumption expenditure reported in national account statistics. Our analysis stops just before the 6Similar methods have been applied to estimate consistent poverty measures when recent household survey data is unavailable and older estimates are considered outdated (Douidich, et al., 2016); to report poverty rates at finer levels of spatial disaggregation (Elbers, et al., 2003); and, to validate official estimates of poverty when comparability of data across surveys is compromised due to changes in instruments (Tarozzi, 2007).

> 7This suggests an extension of the steady progress observed in India over the last two decades, see e.g. Gravel and Mukhopadhyay (2010). However, Dreze and Sen (2012) note that this progress does not extend to all indicators as growth in select nutrition and health indicators, for example, have been more muted. Similarly, Ravallion (2016) notes that despite high growth and a fall in headcount rates in developing countries, the minimum levels of living for the global poor has not moved by much over the past three decades. Castello-Climent and Mukhopadhyay (2013) and Castello-Climent et al. (2018) show that in growth in India is sensitive to changes in tertiary education levels -- suggesting that changes in higher levels of education can impact poverty through the growth channel.

> 8The observed reductions in inequality and poverty are accompanied by major expansion of social security programs in India (e.g. school meals, child care services, employment guarantee, food subsidies, and social security pensions) in the past (see e.g. Dreze and Khera (2017)); and, an expansion of household access to bank accounts, cooking gas, access to toilets, electricity, housing, etc in recent periods, see e.g. Subramanian and Felman (2022).

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