rafmacalaba/data-use-mentions-extended huggingface dataset:
150k rubles, or less than 150k rubles?‖ Q2:** In Russia and the West Bank and Gaza the following was asked: ―Let’s assume that in 2010 your income is twice as now, and the consumer prices also grow twofold. Do you think that in 2010 you will be able to buy more, less, or the same amount of goods and services as today?‖ Q3:** This result for Italy is taken from the 2008 survey (which uses a different sample), since a comparable question was not asked in 2006. New Zealanders were asked a much more difficult (and ambiguous) question: ―Which one of the following is _generally_ considered to make you the most money over the next 15 to 20 years: a savings account, a range of shares, a range of fixed interest investments, or a cheque account?‖ Russian respondents were asked: ―Which is the riskier asset to invest in: shares in a single company stock, or shares in a unit fund, or are the risks are identical in both cases?‖ In Indonesia and India: ―Do you think the following statement is true or false? For farmers, planting one crop is usually safer than planting multiple crops.‖ §Gujurat only; not nationally representative.

7
sharing in each country. A full list of variables, definitions and sources are provided in Appendix I.

# **2.5. Control Variables**

In examining the relationship between competition and systemic stability we control for a number of bank and country level variables. Bank level controls come from Bankscope. For each bank, each year, we calculate bank size (natural logarithm of total assets), leverage (liabilities divided by total assets), market-to-book ratio (market value of assets divided by book value of assets), provisions (loan loss provisions divided by total assets), reliance on deposits for funding (deposits divided by total assets) and profitability (net income divided by total assets). We winsorize all financial variables at the 1<sup>st</sup> and 99<sup>th</sup> percentile level of their distributions to reduce the influence of outliers and potential data errors.

Country level controls are collected from a number of sources. We obtain economic development measures from the World Bank’s World Development Indictor (WDI) database. We use the natural logarithm of GDP per capita to measure the economic development of a country, the variance of GDP growth rate to measure economic stability, the natural logarithm of population to measure country size, and imports plus exports of goods and service divided GDP to measure global integration (Karolyi, Lee, and Van Dijk 2011). In addition, we obtain years in which a country experienced a banking crisis from Laeven and Valencia (2008). We also use stock market capitalization divided by GDP and private credit divided by GDP from the Financial Structure Dataset (Beck, Demirguc-Kunt, and Levine 2010) to control for differences in financial development and structure. As the R-squared measure may be a mechanically linked to the number of cross-sectional observations, we also control for the log of the number of banks in each country.

# **3. Summary Statistics**

In this section we describe our empirical results. Figure 1
# **Repetition**

Tables 5.3 and 5.4 show DiD results for repetition rates. **Table 5.3** focuses on overall repetition across grades. We find that in schools treated in 2017, repetition rates were significantly reduced by 0.28 percentage points. We do not observe significant impacts on overall repetition rates from treatment in 2018.

> 9 The total enrollment in Malawi’s primary schools fell for the first time in over a decade following the closure of schools, with 4,815,286 students enrolled in public primary schools in 2020/21 versus 5,274,819 in 2019/20.

> 10 To identify treated schools, we compare data from EMIS 2016 and EMIS 2017. As repetition and dropout rates are decided at the end of the school year and reported in the following year’s EMIS, for repetition and dropout rates, we compare data from EMIS 2017 and 2018.

> 11 To identify treated schools, we compare data from EMIS 2017 and EMIS 2018. As repetition and dropout rates are decided at the end of the school year and reported in the following year’s EMIS, for repetition and dropout rates, we compare data from EMIS 2018 and 2019.

18
. A second set of adjustments enacted since the 1981 Census focused on the definition of the workforce and agricultural sector.<sup>4</sup> Among our datasets, the 1989 survey follows the 1981 Census classification, the 1994 and 2000 surveys follow the 1991 classification, and the 2005 survey follows the 2001 Census classifications. Our primary focus is on urbanization changes since 1994, and the formal 4 India’s Ministry of Urban Development Report (2011), Kundu (2006, 2009, 2011a and 2011b), Himanshu (2007), Mohan and Dasgupta (2005), Planning Commission (2008), Nelson and Uchida (2008), Bhagat (2011), Ghani (2010), Sivaramakrishna and Kundu (2005) provide greater details. Desmet et al. (2011) examine spatial development of India. Chrandrasekhar (2011) and Mohanan (2008) examine the trends in workers commuting between rural-urban areas. Shaw and Pundit (2001) study segmentation of the informal sector within an industry acrosss urban zones. Gregson et. (2012) provide related evidence from Bangladesh.

5
“mashup indices” (Ravallion, 2010a). The issue in this section is not how this can be done but whether it is useful to do so. One can easily imagine situations in which one would not want a mashup index. Imagine you go for your annual medical checkup. Your doctor does all the usual tests, but tells you that she will base her assessment solely on a single composite index—rescaling and averaging all the test

> 4 The Bank’s Poverty Assessments (significant analytic reports covering virtually all developing countries) typically cover education, health and nutrition and access to infrastructure, in addition to income poverty.

3
Source: World Bank, Global Logistics Indicators Survey, 2005.

Several of these global logistics indicators turned out to be highly correlated with one another, so they are included in the analysis one at a time to avoid multicollinearity problems. In addition, several have very sparse data (notably the time to resolve customs appeals and the frequency of port shutdown). Based on a preliminary assessment of the indicators, a smaller subset was selected for inclusion in the analysis. In addition, in line with the basic gravity model framework, in which trade between countries is an increasing function of their size or income and a decreasing function of distance between the countries (Frankel and Rose 2002), the following indicators are also included:<sup>10</sup>

- GDP of exporting country.

- GDP of importing country.

- Distance between countries (kilometers).

A dummy variable for regional trade agreements or trade blocs is also included, as well as additional variables that are described as they enter the model.

# **Augmented Gravity Model Results**

Specifications 1–3 in table 3 contain the initial set of models fitted, before any of the global logistics indicators or additional variables are included. Specification 1 contains regressions of bilateral trade on GDP in the exporting and importing country. Specification 2 adds the distance variable, and specification 3 adds GDP per capita of the exporting and importing country. The results are qualitatively consistent with those of earlier studies, particularly de Groot et al. (2004, table 2). Also as expected from earlier research, the dummy variable for regional trade agreements (included in specification 4) is significant.

Next, specifications 5–13 in table 3 include, one at a time, the set of global logistics indicators described in the previous section. Most have coefficients with the expected negative sign and are statistically significant; three are not statistically significant. Thus it can be concluded that most of the global logistics indicators have a significant effect on bilateral trade, in the expected
predominantly accessed via desktop computers during weekdays.

3. Unprecedented global diffusion and widespread usage in middle-income economies. Just 16 months since ChatGPT’s release, it has reached 209 of 218 economies worldwide. As of March 2024, the top five economies for ChatGPT traffic are the US, India, Brazil, the Philippines, and Indonesia. The US share of ChatGPT traffic dropped from 70% to 25% within one month of ChatGPT’s debut. Middle-income economies now contribute over 50% of traffic, showing disproportionately high adoption of generative AI relative to their GDP, electricity consumption, and search engine traffic. Low-income economies, however, represent only less than 1% of global ChatGPT traffic.

4. Higher income levels, a higher share of youth population, better digital infrastructure, and stronger human capital are key predictors of higher generative AI uptake. Specialization in digitally tradable services and English fluency are strongly associated with higher chatbot usage.<sup>1</sup> 5. Generative AI tools are already disrupting online traffic patterns and altering user habits. Information, language processing, and professional question and answer websites like Wikipedia, Grammarly, Google Translate, and Stack Overflow experienced significant traffic drops immediately after GPT-4 launch, though some recovery has been observed as sites integrate generative AI capabilities. People are increasingly using ChatGPT for information acquisition and aggregation, skill learning, and language processing, while more complex cognitive and analytical tasks have been augmented by ChatGPT.

Existing evidence in advanced countries shows that AI adoption by firms remains very low and is dominated by large firms and young firms. Acemoglu (2024), Babina, Fedyk, He, and Hodson (2024), and Goldfarb, Taska, and Teodoridis (2023) used online job posting data to measure firms’ AI adoption and subsequent effects. Miric, Jia, and Huang (2023) and Webb (2019) used patent data as a proxy for AI adoption. Zolas, Kroff, Brynjolfsson, et al. (2021) and McElheran, Li,
**Table 5: Height by birth cohort, women age 15-49.**

|**Age in 2000**|**Age in 1975**|**Average height**|**% <150cm**|
|---|---|---|---|
||||all|
|15-19||152.125|33.45|
|||[0.130]|[0.0114]|
|||1685|1685|
|20-24||152.88|27.67|
|||[0.1593]|[0.0145]|
|||951|951|
|25-29|0-4|153.328|26.04|
|||[0.1526]|[0.0136]|
|||1040|1040|
|30-34|5 to 9|153.545|25.28|
|||[0.1586]|[0.0128]|
|||1137|1137|
|35-39|10 to 14|152.638|30.01|
|||[0.1587]|[0.0138]|
|||1040|1040|
|40-44|15 to 19|153.008|27.99|
|||[0.1743]|[0.0150]|
|||890|890|
|45-49|20 -24|152.896|27.88|
|Total||[0.2030]|[0.0171]|
|||681|681|

Standard errors in brackets, with sample sizes.

**_Source_ :** Cambodia, Demographic and Health Survey (2000).

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a market rate of return. As a result, fiscal costs are lower since workers who opt for benefits under the old PAYG system will have to surrender the accumulated balances in their individual accounts to the Government.

# **_Fiscal_** _Costs:_ **_Assumptions and Scenario Analysis_**

**Estimating the size of contributing population.** The first step in the estimation of the fiscal costs was to project the size of the future population, which would enroll in the IMSS scheme. IMSS does not publish this information and demographic projections prepared recently by the Luis Donaldo Colosio Foundation were used. These projections show that, in the long run, the population affiliated to the IMSS as a proportion of economically active population (EAP) would grow from 28% to 38% by 2031.

The second step was to determine the contributors age distribution. For the transition population this was obtained using the IMSS tables of number of affiliates (1982-1993) broken down by age. The proportion by chronological age was established by applying moving averages to the 1982-1993 data, which show very small year-to-year variation. For new entrants, IMSS' _Age Frequency Table_ for new affiliates was used which shows that entrants are predominantly under 30 years old. The age distribution of the contributing population will change as function of new entrants' age. Entrants' age distribution is assumed to be the same throughout the projection period.
.6|61|61|86|94.1|
|Distribution losses|% production|31.5|39|53|51|34.8|
|Cost recovery|% total costs|57.2|—|46|—|89.5|
|Operating cost recovery|% operating costs|80.1|—|64|—|125.2|
|Labor costs|connections per<br>employee|168.8|63|49|62|175.9|
|Total hidden costs as % of revenue|%|169|185|187|148|67|
|||**2004**|**2009**|Non<br>water|-scarce<br>resources|Other<br>developing<br>regions|
|Average effective tariff|U.S. cents per m<sup>3</sup>||57|57|80|3.0–60.0|

_Source:_ AICD water supply and sanitation database (http://www.infrastructureafrica.org/aicd/tools/data).

Access figures calculated by the AICD using data from Demographic and Health Survey (1994) and Multiple Indicators Cluster Surveys (2000 and 2006), as published by the WHO/Joint Monitoring Program (JMP) in March 2010.

_Note:_ Domestic water consumption is calculated as a weighted average of the consumption in Bangui and seven other centers served by SODECA. A country is considered to have non-scarce water resources if the renewable internal freshwater resources per capita are greater than 3,000 millimeters.

m<sup>3</sup> = cubic meters.

= Data not available.

More than three-fourths of the population relied on latrines as of 2006, but due to data constraints and the difficulty of tracking investments made by nongovernmental organizations (NGOs), it is not possible to make a clear separation between improved and traditional latrines.
the Zone Colon leads to an additional US$0.42 increase in value added from non-traded goods and services (national economy simulation).<sup>17</sup> This gain is due mainly to consumer spending effects as incomes earned in various activities are spent in the domestic economy. Multipliers are smallest (1.30 to 1.64) in sectors such as the Panama Canal sector, mining and textiles) where there are few production linkages (much of the inputs are imported; much of the outputs are exported) and much of the income accrues to formal capital (enterprises).<sup>18</sup> In contrast, the multipliers for the fruits, shellfish and other agricultural exports are especially large because much of the income earned accrues to rural households who spend a high proportion of their incomes on nontradable goods and services in the local economy. The multiplier for the tourism industry (hotels and restaurants) is the largest of all the sectors: an additional US$1 in value added (approximately US$2.80 in total tourism spending) results in a total US$2.87 in total incomes. This large multiplier is due to strong backward linkages in terms of demand for local food products as well as forward linkages of household spending.<sup>19</sup>

> 17 The value added multipliers vary little across region because the input-output structure and patterns of factor payments to households are assumed to be the same across regions (since regional input-output tables are not available).

> 18 In these multiplier simulations, investment is held constant and income gains to enterprises are not distributed as dividends (income) to domestic households.

> 19 These simulations may understate the multiplier somewhat because the hotels and restaurants is exogenously fixed in supply. Thus, there are no demand linkage multiplier effects for this sector (apart from the initial simulated increase in demand and output). Note that in the regional simulations, it is assumed the all demand for elastically supplied goods
., 2019).<sup>15</sup> Large algal blooms can devastate ecosystems, often resulting in hypoxia or dead zones, a condition that arises when water bodies lack sufficient oxygen. The legacy effects of nitrogen pollution on the environment can also endure decades after nitrogen inputs have ceased, with long time lags between the adoption of conservation measures and any measurable improvements in water quality (Van Meter et al. 2018; Basu et al., 2022). Not surprisingly, according to the FAO, in many countries the biggest source of water pollution today is agriculture — not cities or industry — while worldwide, the most common chemical contaminant found in groundwater aquifers is nitrate from farming (Mateo-Sagasta et al., 2017).

The results in Table 2 show that an increase in agricultural fertilizer use leads to substantial deterioration in water quality such that there is a strong and positive impact of nitrogen fertilizer use on the concentration of nitrogen in water. These estimates are robust across multiple specifications with elasticities ranging from 0.16 to 0.34, suggesting that a 10 percent increase in nitrogen fertilizer leads to a 1.6 to 3.4 percent increase in the concentration of nitrogen pollutant. These pollution elasticities are consistent with estimates from prior econometric literature that find adverse effects of nitrogen fertilizer use on water quality in country-specific settings (Paudel and Crago, 2021).<sup>16</sup>

> 15 Although the dominant source of nitrogen pollution in the water is the agriculture sector, many other diverse sources also contribute to its proliferation from livestock waste, fossil fuel combustion to untreated wastewater. As cities grow and become denser, the threats of nitrogen leaching from below-ground septic tanks, human sewage, urban wastewater, and urban stormwater runoff are also expanding.

> 16 The authors construct an empirical model on determinants of ambient water quality using over 2.9 million pollution readings and find that a 10 percent increase in the use of nitrogen fertilizers (kg) leads to a 1.47 percent
R&D. Instead, they analyze surveyed formal firms’ responses to whether “anticompetitive and informal” practices are an obstacle for their business. The authors acknowledge that the question is noisy because anticompetitive practices can result from the behavior of the formal sector firms. Firms’ view of informality as an obstacle may also be driven by the perceived impact of informal activity on total revenues, profits, or productivity rather than R&D activity. Moreover, La Porta and Shleifer (2008) use old data collected between 2002 and 2007.

Rozo and Winkler (2019) find that in Colombia, an increase in internally displaced persons (IDPs) adversely affected the total output of the formal sector firms. Their interpretation is that an increase in IDPs entails an expansion of the informal sector, which in turn adversely affects the total output of firms in the formal sector. Loayza (1996) suggests that a large informal economy may lower tax revenues and increase public service congestion, which in turn, can negatively affect firms in the economy. In Loayza’s (1996) study however, these negative effects of informality do not explicitly result from the informal competition faced by the formal sector firms.

The present paper contributes to the above literature in several ways. First, it analyzes the impact of informal competition on the likelihood of spending on R&D by formal manufacturing SMEs. To the best of our knowledge, this is a relatively unexplored area of research.

Second, informal firms are known to be very small and therefore unlikely to pose any problems for large firms. Thus, a proper analysis requires that we distinguish between the effects of informality on SMEs and large firms. Studies using macro-level data cannot capture this heterogeneity. We use cross-country comparable firm-level survey data for our analysis. These data allow us to distinguish between SMEs and large firms. Our results show that informal competition affects SMEs but not the large firms.

5
The share of income from nonagricultural manual labor falls from nearly 30 percent among the poorest to only around 10 percent among the richest. But, in the segmented model, the wage paid to such labor is much less responsive to food prices than in the model with perfect intersectoral mobility. In short, ruling out labor market segmentation empirically does have consequences for how we think about the distributional effects of food price increases.

# **4.3 Public Distribution System**

Assessing the role of the PDS in buffering price shocks provides a useful comparator as well as being of interest in its own right. Under the PDS, eligible households (generally, those below the poverty line) can purchase fixed rations of either rice, wheat, or sugar in “Fair Price Shops” at below-market (but non-zero) prices. If PDS prices rise at the same rate as non-PDS prices, then the above formula for _ε_ applies. However, insofar as PDS prices rise more slowly than non-PDS prices, or do not rise at all, we need to adjust by where _θj_ = 1 _−_<sup>∆</sup><sup>_<u>p</u>_</sup> ∆<sup>_<u>j,</u>_</sup> _p_<sup>_P DS_</sup> _j_ and _νj,PDS_ is the expenditure shares for PDS purchases of commodity _j_ evaluated at the PDS price. Thus, to take the extreme case, if all PDS prices stay fixed while market prices rise, then instead of _νA_ in the calculation of _ε_ , one would use the food share _net_ of the PDS expenditure share. To get _θj_ , I take the state mean unit values from the NSS-CES for the respective commodity-types (PDS or non-PDS) in each successive round (61st and 66th). Thus, my estimate of _θj_ is the ratio of percentage changes between 2004-05 and 2009-10 of state mean unit values for PDS and non-PDS purchases
negative demand shocks during the crisis reported less strict enforcement of labor regulations relative to other firms (Park, Giles, and Du 2011).

In addition, recognizing that migrant workers and new graduates may be hit hardest by layoffs and lack of employment opportunities in the wake of the crisis, the Ministry of Human Resources and Social Security expanded training programs to promote employment among these groups.<sup>13</sup> Targeted recipients included employees of enterprises facing financial difficulties, returned rural migrant workers without employment, unemployed registered college graduates who were new market entrants, and unemployed rural migrant workers living in urban areas. Furthermore, in early February 2009 the Central Committee of the Communist Party issued a document promoting employment of migrant workers.<sup>14</sup> Enterprises were encouraged to maintain migrant employment, and those enterprises facing difficulty were allowed to adopt reduced or flexible work hours and provided with subsidies for on-the-job training to maintain employment. Laid-off migrants who returned home were provided with subsidized credit, favorable tax treatment, and consultations to help with starting new businesses. Temporary income assistance through expansion of the rural minimum living standard support program (known as the _dibao_ ) also helped sustain migrants who returned home without jobs.

Unfortunately, neither active labor market programs nor interventions to support employment were implemented in a way that allowed proper evaluations of impact. Indeed, the only public documentary evidence on such programs, training interventions, and extensions of credit in the public domain is in the Ministry of Human Resources circulars directing local governments to implement such policies in a manner consistent with local conditions.

According to evidence from the rural household survey data, the robust recovery of labor demand following the stimulus was most important for the reemployment of rural registered workers who had lost jobs in the immediate wake of the crisis. Although the counterfactual gap in employment was still large in the first four months of 2009 (as shown in Figure 6), it was already beginning to narrow in percentage terms. By April 2009, Huang et