rafmacalaba/data-use-mentions-extended huggingface dataset:
power grid is green, then this purchase decision can decouple consumption gains from greenhouse gas production.

The state of California provides unique zip code level data on vehicle registrations.<sup>1</sup> The data are available from October 2018 and January 2020. In each of these two cross-sections, the data report each zip code’s count of vehicles by fuel type and by model year. In total, the data set includes over 30 million vehicles. We have merged year 2018 Internal Revenue Service zip code data on the household income of tax filers.<sup>2</sup> For roughly 1,900 zip codes in California, we merge in data to identify the 25% highest income zip codes in the state. The zip codes are sorted by the percentage of tax filers with incomes over $200,000. In Table 5, we report the empirical distribution of vehicle fuel types in 2018 and 2020 and we report these tabulations for rich and non-rich zip codes. The first key finding is that even in rich, educated and progressive California, the percentage of electric vehicles in the fleet is very low. The second fact is that the share of electric vehicles is growing over time. At each point in time, richer zip codes have a much larger share of electric vehicles.

The California vehicle registration data also reports the model year distribution of the vehicles by zip code. In the bottom of Table 5, we report the empirical distribution of vehicles by three categories; vehicles built before 2010; vehicles built between 2010 and 2015 and “new” vehicles. The key point that emerges here is the durability of the capital stock. The vehicle stock is old and predominantly gasoline based. Given that cars can live on for decades, even California stakeholders have an incentive to oppose higher gas taxes.

In this section, we have presented new evidence documenting the growth of EVs but the very small market share of this product even for rich people. We have also emphasized the durability of
Poverty is defined based on whether per capita household welfare, converted to international dollars using the revised 2011 PPP conversion factors, falls below the $1.90 per day poverty line (Ferreira et al, 2015).<sup>4</sup> We consider all children in poor households to be poor and all children in non-poor households to be non-poor; therefore, as is standard, child poverty is defined as the percentage of children that live in poor households. This assumes all household members enjoy the same standard of living, which as discussed in section four below, may understate the full extent of child poverty.

The face of poverty is young, as children ages 0 and 17 comprise just over half the number of extreme poor in the sample. Given that 767 million persons are estimated to be extremely poor in 2013, our best estimate is that 385 million of them are children. That is, 19.5 percent of all children in the sample are poor, as opposed to 12.5 percent of all persons and 9.2 percent of adults. Poverty rates are 21 percent for 0 to 4 year olds, increase slightly to 21.5 percent among 5 to 9 year olds, and then steadily decline for successively older age groups. The extreme poverty rate for adults is 9.2 percent. We also examine moderate poverty, defined as those living between $1.90 and $3.10 per day. The sum of extreme and moderate poverty rates is substantially higher for children, 44.5 percent live on less than $3.10 per day, as opposed to 26.6 percent of adults.

These results, based on household welfare per capita, immediately raise the fundamental question of how child poverty rates depend on adjustments for differences across households in size and composition.<sup>5</sup> Per capita measures give children the same weight as adults when assessing household needs, and do not allow for larger households to benefit from economies of scale
For these simulations we start with<sup>World Bank population projections for</sup> China, 1995-2100.1 Our baseline and alternative assumptions<sup>about the rate of</sup> real wage growth, interest rate and<sup>other economic and policy parameters over</sup> this period are given in Table 2 . In most countries the interest rate tends<sup>to</sup> exceed the rate of wage growth by more than<sup>2%, over long periods. But in China</sup> the opposite is true at present. China's rate<sup>of real wage growth over the past</sup> decade has been almost 6% annually, while<sup>the real interest rate available to</sup> domestic savers in recent years has<sup>been negative, due to controlled nominal</sup> rates that are less than the inflation rate<sup>(1994 Statistical Yearbook of China,</sup> Beijing: State Statistical Bureau). In<sup>our baseline simulations we accordingly</sup> use a rate of wage growth that exceeds<sup>the interest rate initially and vice versa</sup> after 2011, assuming that financial<sup>markets have been reformed and wage growth</sup> has slowed down in the interim. We<sup>also carry out sensitivity analysis to these</sup> variables by experimenting with scenarios in which wage growth is<sup>lower and</sup> financial market reform faster or slower.<sup>2</sup> The continuation of current evasion<sup>and exemption rates of 25% are also</sup> assumed. Enforcement techniques will probably<sup>improve in the future, reducing</sup> these leakages, but the expansion of the nonstate<sup>and informal sectors will have</sup> the opposite effect, so as a baseline the
supervisory practices changed since the previous survey in response to the global financial crisis?

Based on a series of univariate tests, we find significant differences between crisis and non-crisis countries in several aspects of regulation and supervision. First, crisis countries had less stringent definitions of capital and lower actual capital ratios. Second, banks in crisis countries faced fewer restrictions on non-bank activities such as insurance, investment banking, and real estate. Third, regulations concerning the treatment of bad loans and loan losses were less strict in crisis countries. Finally, in crisis countries, there were weaker incentives for the private sector to monitor banks’ risks.

Comparing regulation and supervision before and after the global crisis, we see that the overall regulatory response to the crisis has been slow, although we find evidence of a number of significant changes. In particular, capital ratios increased, deposit insurance schemes became more generous, and reforms were introduced pertaining to bank governance and bank resolution. In some of these areas, we observe differences between crisis and non-crisis countries. These findings suggest the reform process is ongoing and there is still room for changes in both regulation and supervision frameworks and incentives for private monitoring.

This paper is related to studies that have examined previous rounds of the BRSS. Barth et al. (2001) present the data from the first BRSS. Using this survey, Barth et al. (2004) assess the relationship between specific regulatory and supervisory practices and banking-sector development, efficiency, and fragility. With the same dataset, Barth et al. (2006) show that restrictions on the entry of banks, government ownership of banks, and restrictions on bank activities hurt banking system performance. Finally, Barth et al (2008) compare the first three BRSSs (covering the period 1999-2005) and conclude that while many countries strengthened 3
state sought to make society “legible” through the creation of permanent last names, the establishment of cadastral surveys and population registers, etc. Similarly, modern governments seeking to understand their potential tax base must increase coverage of foundational identification systems, business registries and property registries.

Detection capacity is closely related to identification and builds on it. After the presence of a taxpayer or tax base is known to the state, it must then verify the income, assets, and

> 1 Of the 61 Tax Administration Diagnostic Assessment Tool (TADAT) assessments conducted in lower income countries from 2013 to 2020, only 20 percent of tax authorities had a score of good or very good for knowledge of taxpayer database and 5 percent did so for accuracy of information in taxpayer registry.

2
differences in maize prices that farmers are likely to face. The price of nitrogen, , is computed NN using the prices and nitrogen composition of _chitowe_ (NPK 23:21:0 +4S) and the urea fertilizer, PP the two main fertilizers used in maize production. Following Xu et al. (2009), let _f_ be the amount of each of _chitowe_ and urea required for a kilogram of nitrogen. Given the 1:1 application ratio of _chitowe_ and urea and their nitrogen components (23% for chitowe and 46% for urea), we have the following expression: ; and solving for _f_ results in . This means that a kilogram of nitrogen costs approximately 1.449 kilograms of each of 23%ff+ 46%ff= 1 ff= 1.449k _chitowe_ and urea; hence [ = 1.449 ∗(p . NN PP ppccee ooff cchppiiooiiee+ p ppccee ooff uuppeeaa)] Whether or not fertilizer is likely to be profitable depends on outcomes that are uncertain when fertilizer decisions are made, as well as costs associated with the use of fertilizer that are unknown to the analyst. If these uncertainties and unobserved costs are assumed to be zero, fertilizer use is deemed profitable if MVCR is at least one, an indication that an increase in the rate of fertilizer application will increase income from maize production. In the face of these uncertainties and unobserved costs however, an MVCR of at least 2.0 (meaning a risk premium of one) has been recommended in the literature to be required in order for fertilizer to be profitable (Xu et al., 2009; Sauer and Tchale, 2009; Bationo et al., 1992; FAO, 1985). We discuss the implications of our results when profitability is assumed to be obtained with an MVCR of 1.0 and 2.0.

# **Data and Sample Selection**

The data used in the analyses is a two-wave, nationally representative, panel dataset collected by the national Statistical Office of Malawi
of growth) was developed first as a management tool and later became an effective supervisory mechanism.<sup>22</sup> PEARLS results in _objective measurements,_ whereas the CAMEL approach involves some degree of _subjective judgment_ by analysts or examiners particularly on management quality and capability. In addition, private commercial banks have developed their own financial monitoring and evaluation systems which isolate and measure different categories of risks.

55. The main thrust of this paper is to promote a standard application of risk management principles which would be useful to an institution's management and its governing board or trustees, shareholders or members, external auditors, the regulatory authorities, institutional creditors, donors and the general public.<sup>23</sup> This paper emphasizes that _the responsibility for risk management rests principally on voluntary regulation through internal governance, rather than on external supervision by regulatory authorities._ Risk-based financial regulation should identify and specify the following aspects:

- a) The particular risks that are most relevant to MFIs,

- b) For each relevant risk, the key indicators that are most important for risk management in MFI operations,

- c) The ranges of values and their trends over time which would be useful to directors and managers responsible for internal governance in monitoring the financial health ofMFIs that they manage, and

- d) The ranges of values and their trends over time which would be invaluable in establishing regulatory guidelines to be used by external supervisors who have the mandate to regulate MFIs under their jurisdiction.

56. Table 5 (together with its accompanying Notes) in the pages that follow summarizes the categories of risk and range of values of financial risk indicators for the three broad classes of MFIs in this paper's regulatory framework model. Table 5 highlights (i) the observed value ranges of selected financial risk indicators, (ii) recommended value ranges suitable for consideration in internal govemance and, where appropriate or warranted, (iii) suggested threshold values with respect to external regulation for each of the three categories
ENIGH), the sampling design was stratified in several stages (where the final selection unit was the household) and with proportional probability to size.<sup>3</sup> This statistical construction allowed us to make comparisons among different years. Moreover, this survey is structured to generate a panel data set that conforms with a rotator or rotating panel (a fifth of the total sample goes out and a new one comes in every quarter). Hence, the panel data follow the same household throughout five quarters.

> 3 For this it was necessary to use weights or expansion factors.

36
Table 4 contains daily amounts of per capita gasoline expenditure and the monthly household level of consumption among the population in the Islamic Republic of Iran. A strong gradient in expenditure and consumption of gasoline is clearly apparent, consistent with transportation ownership. The richer population is more likely to own cars and tends to spend and consume more gasoline compared to the less wealthy population who either do not own transportation or own motorcycles only. Thus, the richest 10 percent consume about 116 liters per month on average compared to 20 liters consumed by the poorest 10 percent. Another way to look at this is to check the distribution of population by gasoline consumption (Figure A1 in the annex). The largest share of population without gasoline consumption is from the poorest decile – 61 percent, compared to 22 percent for the richest decile.

Table 4. Expenditures and consumption of gasoline in the Islamic Republic of Iran by deciles

|deciles|Dailyexpenditure ongasoline,IRRper capita|Quantity,litersper monthper HH|
|---|---|---|
|poorest|1,476|20|
|2|2,493|31|
|3|3,482|41|
|4|4,098|47|
|5|5,270|57|
|6|6,190|65|
|7|7,349|74|
|8|8,935|84|
|9|10,982|98|
|richest|15,487|116|
|Total|6,576|68|

Source: Authors’ calculation using forecasted HEIS 2018/2019. Note: Overall daily consumption of gasoline is estimated to be around 53.3 mln liters. This is quite close to gasoline consumption numbers reported by Iranian authorities.

As a result of higher consumption of gasoline, richer people are the main beneficiaries of fuel subsidies as shown in Figure 4. The richest decile gets a 10 times higher direct benefit per capita through subsidized gasoline compared to the bottom decile
of this small percentage difference on how investors actually assessed startups. To provide more insight, the first author conducted 45-minute semi-structured interviews with investors in one region, to examine how investors conducted evaluation, and how they decided to give startups a high or low score. After interviewing all investors in the treatment and control program in one region, we found that many investors evaluated static startup elements of the venture to assess its potential. For example, a male investor explained: “I gave AA a four [top score] in most of the categories... I really like their solution... it has a lot of potential for scaling... I went through their website... I was quite impressed with the profiles of people that work in the team... their business model too.” A female investor explained that she rated a startup well when the problem was convincing: “I rated BB high... because I think the business idea is really necessary... I see its use and purpose.” These investors used similar criteria to that elaborated by Gompers and coauthors in their survey of earlystage investors (2020). In the control group, all nine investors evaluated static criteria (100%). As shown in Table 6, in the treatment group, three of the seven treated investors (43%) also described how they gathered data on static criteria in at least one of their responses.

Some investors took a more dynamic lens, focusing on progress or improvement when scoring ventures. A male investor described how he had ranked a startup well because he had observed improvement over time: “The question is, have you seen improvement in them during this business program? ... I’ve been in a breakout room with YY twice. And the suggestion I had noted to her in the first breakout room ... was repeated with a group of mentors... they asked a similar question.... I noted that when she
# **3.2 Polygyny Data and Measurement**

Our polygyny data are from the Fourth General Census of Population and Housing (2009). This census contains information on the demographics and socio-cultural characteristics of individuals and the communes in which they live. We use this information to construct our polygyny variable. Polygyny is measured at the commune level and is defined as the prevalence of polygyny in the commune. Measuring polygyny at the commune level is highly relevant given the observed covariance of marriage structure and ecological conditions which often varies across geographic units such as communes. For example, Lawson et al. (2015) find that in northern Tanzania, predominantly polygynous ethnic groups tend to cluster around agro-climatic zones prone to droughts and economic hardships, whereas predominantly monogamous groups tend be clustered in relatively high rainfall, highly fertile agro-climatic zones. This finding suggests that measuring polygyny at the commune level gives a sufficiently high resolution of the within-cluster correlation of marriage structure and its non-correlation across clusters. Our interest in polygyny stems from its hypothesized interaction with drought risk in communities dependent on rainfed agriculture for their livelihoods. To avoid any possible collinearity between drought and its interaction with polygyny, we center our polygyny variable by subtracting from it its mean, and then dividing by its standard deviation (Iacobucci et al., 2016; Dalal and Zickar, 2012). This transformation does not change the interpretation of the coefficient of our interaction term (Allison, 1977).<sup>9</sup> Thus, our results can be interin units of standard deviation. More our standard variable is as preted formally, _Polyj_ follows: where _Polygynyj_ is the rate of polygyny in commune _j_ , _Polygyny_ its mean and _σPolygyny_ the standard deviation.

> 9Multicollinearity is known to undermine the statistical significance of an independent variable. When this problem is present, small sampling fluctuations in the estimates of the covariance can result in great variability in the values and signs of least squares estimates of the coefficient (Kromrey and Foster-Johnson, 1998).

14
# 1 Introduction

Recent research on ...rm-level exporting suggests that the idiosyncratic interplay between ...rms and markets plays an important role in shaping trade patterns. Examining detailed data on the exports of French ...rms, Eaton et al. (2011) show that while a multipledestination Melitz (2003)-type model does a good job at explaining several empirical regularities, it fails to come to terms with some important features of the data: (1) ...rms do not enter markets according to an exact hierarchy; and (2) their sales where they do enter deviate from the exact correlations that the basic model would insist upon.<sup>1</sup> To account for these facts, they introduce market and ...rm-speci...c heterogeneity in entry costs and demand. But while the modi...ed framework is more consistent with observed trade patterns, there is still little evidence on how speci...c potential sources of such heterogeneity in entry costs and demand a¤ect the export performance of individual producers.

In this paper, we match historically-determined emigration stocks with detailed ...rmlevel data from Portugal to examine the e¤ect of migrant networks on export participation and intensity across markets and ...rms. Portugal o¤ers unique features for this investigation. First, the motives and timing of the sizable emigration ‡ows observed in the country during the Estado Novo authoritarian regime, along with their steep fall in the aftermath of the democratic revolution of 1974, mitigate the concern that emigration stocks might be endogenous to current trading relationships.<sup>2</sup> Second, emigration ‡ows came predominantly from the Northern region of Portugal, suggesting that ...rms located there are more likely to have close ties with the emigrants. These historical features allow us to investigate not only whether migrant networks matter for export outcomes, but also if they constitute an important source of market and ...
is no database covering all ODA disbursements per sector. In terms of commitments, the 1990s saw a major drop. For instance, infrastructure commitments from multilateral development banks fell from $18.0 billion in 1996 to $13.5 billion in 1999, though by 2002 they had recovered to about $16.0 billion. But even at its peak, such financing was too small relative to needs. At this point the best that can be hoped for is a significant countercyclical effect—with ODA at least sufficient to offset swings in private sector contributions, as well as leverage funds from private sources.

The private sector now provides more infrastructure financing than does ODA, but there are no official statistics to demonstrate this without a doubt. Research conducted at the World Bank has generated some data on the new contribution of the private sector, but this data is imperfect because much of it focuses on commitments rather than disbursements. Knowing that many signed projects are cancelled before they are implemented, the gap between commitments and disbursement is likely to be high—but no one really knows how high simply because there is no data, again except for Latin America thanks to Easterly and Serven (2003). According to a worldwide database, during 1990–2002 private sector commitments to infrastructure in developing countries totaled $805 billion, or $62 billion a year. But this average masks enormous fluctuations. Private commitments rose sharply until 1997, then fell quickly in response to East Asia’s financial crisis. In recent years they have hovered around levels like those in the mid-1990s. For example, in 2002 private commitments totaled $47 billion—the lowest level since 1994. This suggests that private investment accounted for only 10–15 percent of estimated investment needs. In addition, most of these investments have gone to energy and telecommunications (in terms of sectors) and to Latin America, East Asia, and (to a lesser extent) Eastern Europe (in terms of regions).

> 9 U.K. Department