rafmacalaba/data-use-mentions-extended huggingface dataset:
years since 2008 in 12 provinces in Vietnam. From 2008, the survey included a panel component. We used data from the 2008, 2010, 2012, and 2014 rounds of the survey. As per this data, 99.5 percent of RTP3 road improvements occurred between 2009 and 2014 (Table A.1). Having a panel allows us to use household and individual-level fixed effects, which requires less stringent identification assumptions. The panel component of the VARHS is the main reason we chose it over another household-level data set from the same period: the Vietnam Household Living Standards Survey (VHLSS).

The sample in the VARHS was chosen in 2006 to be representative of the population in rural areas in the sampled provinces.<sup>9</sup> In 2008, this sample was expanded by 1,000 households in five additional provinces<sup>10</sup>

> 9 Đắk Lắk, Đắk Nông, Điện Biên, Hà Tây, Khánh Hòa, Lai Châu, Lâm Đồng, Lào Cai, Long An, Nghệ An, Phú Thọ, and Quảng Nam.

> 10 Lào Cai, Điện Biên, Lai Châu, Đắk Lắk, and Đắk Nông.

7
The GEGI runs from 0% to 100%, with a bigger number indicating a larger gender employment gap. −0 • If there is no female employment at all ( ), then × 100% = 100% (the largest +0 LLMM possible gender gap). LLFF = 0, LLMM = LL GGEEGGII= LLMM • If there is equal employment across females and males, then (no gender gap at all). The GEGI can be calculated directly from employment data, measured as the number of people (e.g., is MM FF LL = LL GGEEGGII= 0 the number of males employed, and is the number of females employed and is total number of people MM LL employed). But often labor market data are reported as the working-age employment-to-population ratio for each FF LL LL gender, which I denote with a lower case: and . In that case, Equation 1 can be rearranged as Equation 2, which is equivalent, and is used in the empirical application later in this paper.<sup>4</sup> MM MM MM FF FF FF EE = LL /PPE EE = LL /PPE (2) B B G G E E E × 100% llMM−llFF Note that the GEGI formula assumes an equal population of men and women, which holds in most BB G EEG EE GGBBEE IIG GGII (GGEEGGII) ≡ llMM+LLFF countries. Appendix 4 considers an adjusted basic GEGI that should be used for eight countries with a higher male population share (Bahrain, Kuwait, Maldives, Nepal, Oman, Qatar, Saudi Arabia, United Arab Emirates).

# **2.2 Economic interpretation**

The basic GEGI has a simple and appealing economic interpretation: _the increase in long-run GDP per capita from closing the gender gap in employment rates_ . For example, if GEGI=19% (the cross-country average), it would imply that long-run GDP (or GDPPC) would be almost 20% higher if the employment gender gap was
extent that banks either extended loans or overdrafts very early in the crisis to firms that they believed had long-term potential (if they could survive temporary liquidity problems) or ended lines of credit or loans for firms that they believed were the most likely to fail during the crisis, this could affect our estimates in the survival regressions. Still, it is likely that most of the repercussions of the crisis in terms of access to credit for firms in ECA had not been felt at the time of the last round of the BEEPS survey. Moreover, if a firm reported having a loan at the time of the BEEPS it must have applied for and received it prior to the survey.

Because the financial constraints regressions are designed to summarize firms‘ access to financial services, this set of variables represented by F is obviously less relevant. However, and as noted above, we do include proxies for the availability of internal funding in those regressions. C represents a matrix of country characteristics. Because the survival analysis is based on a cross-section and because the FCS was done in only six countries, it is not possible to include a full set of country-level controls in the survival regressions. In the survival analysis, we therefore include country dummies instead of country controls.

Because the BEEPS data offer time series variation and cover a much broader set of countries, we are able to include a rich set of country-level variables in the financial constraints regressions. These include GDP per capita, the KKM index of institutional development,<sup>20</sup> inflation, and GDP growth. All except inflation are likely to be associated with less severe financial constraints. We also include population and population density in the constraints regressions, though it is less clear how those variables are expected to affect financial constraints. We interact the GDP growth variable with dummy variables for the year of the survey to test whether that variable had differential effects on financial constraints in crisis and non-crisis years, in line with some predictions from the literature.

The country variables
Currie and Schmieder, 2009), reduces human capital formation and affects long term human capital outcomes (Bharadwaj et al., 2017; Grineski et al., 2020; Currie et al., 2009). Moreover, these negative effects are typically worse for people in lower socio-economic backgrounds.

A small subset of the literature on negative health impacts of air pollution has focused on flaring, often in the context of unconventional oil and gas production (fracking). Cushing et al. (2020) used satellite observations of flaring from unconventional oil and gas wells in Texas, and link to administrative birth records for pregnant women residing up to 5km from a flaring site. Exposure to frequent nightly flare events was associated with 50% higher odds of preterm birth and shorter gestation compared with no exposure, with effects especially for Hispanic women. Flaring was not associated with reduced birthweight among term infants after controlling for gestational age. Hill (2018) focused more broadly on shale gas development in Pennsylvania and found lower birth weight among infants born to mothers who live close to shale gas wells but an insignificant effect on birth term. Their difference-indifferences model used mothers living within 2.5 km of a shale gas well or permit prior to drilling as a control for treatment of living in the same radius after drilling began (the counterfactual change in infant health is estimated using births prior to drilling at the same distance from the well bore location or permitted location). The risk of low birth weight increased after drilling began. Colborn et al. (2014) focused on air quality around fracking sites in a rural western Colorado area where residences and gas wells co-exist. They found concentrations of pollutants well above critical levels that are associated with lower developmental and IQ scores in prior research in urban areas.

The studies described above are for localised settings in developed countries in the context of fracking but gas flaring in developing countries is mostly in the context of conventional oil and gas production. This distinction may matter as fracking is associated with other pollutants (
implies that for the same level of depth the UK exported or imported relatively more (less) than the average country in the sample. Both coefficients capture the average effect of depth after the agreements enter into force and assume linear effects on trade, with a percentage point increase in depth having similar effects for deep and shallow agreements.

Two caveats need to be kept in mind. First, the model assumes that the marginal effect of an additional provision is the same regardless of what type of provision is included. It is well possible that provisions in deep trade agreements have a different impact depending on how relevant they are for trade. Provisions on standards, investment or competition are likely to have a larger impact on trade than provisions that do not pertain to trade or economic cooperation (e.g. statistics, cultural cooperation). Second, we expect the short‐run impact to be smaller than in the longer term, because it takes time for trade flows to respond to changes in trade costs (Johnson and Noguera, 2014; Baier and Bergstrand, 2007).

# a. Goods and services trade

We begin our investigation from a more conventional analysis of gross trade flows. The first 3 columns of Table 4 report the PPML estimates of our baseline regressions of depth on gross trade. The gravity equation is augmented with the interaction of depth with a dummy that identifies observations for the UK as importer or exporter. The results in column 1 suggest that country pairs that signed the deepest PTA increased their total bilateral trade by 42 percent. The UK was not affected more than the average since the coefficient of the interaction is not significantly different from zero. In other words, the depth of UK trade agreements with the other EU members and with other partners with which the EU had signed PTAs increased UK trade by 42 percent –not differently from other countries.

The data in WIOD allow to split total trade into trade in goods and trade in services. Columns 2 and 3 report the coefficients of depth and the interaction
WPS3795 secondary source of repayment, but the lending technology would still be relationship lending if the lending decisions are primarily based on soft information gathered over the course of a relationship.<sup>1</sup>

# **A. Financial Statement Lending**

Financial statement lending is a transactions technology based primarily on the strength of a borrower’s financial statements. There are two requirements for this technology that depend on hard information. First, the borrower must have informative financial statements, such as audited statements prepared by reputable accounting firms according to widely accepted accounting standards such as GAAP. Second, the borrower must have a strong financial condition as reflected in the financial ratios calculated from these statements. The loan contract that arises out of the analysis of these financial statements may reflect a variety of different contracting elements such as collateral and personal guarantees. However, under financial statement lending, the lender views the expected future cash flow of the SME as the primary source of repayment. Financial statement lending, unlike other lending technologies, is reserved for relatively informationally transparent firms.

# **B. Small Business Credit Scoring**

Small business credit scoring is a transactions technology based primarily on hard information about the SME’s owner as well as the firm. The owner information is primarily personal consumer data obtained from consumer credit bureaus. This is combined with data on the SME collected by the financial institution and often from commercial credit bureaus. The data are entered into a loan performance prediction model, which yields a score, or summary statistic for the loan. The models are usually designed for credits up to $250,000, but many institutions use them only for credits up to $100,000. The technology is relatively new – it was not widely used in the U.S. until the mid-1990s.

This technology may be applied to very opaque SMEs, given that much of the information is based on the personal history of the owner, rather than the SME. Consistent with this opacity, recent
appliances covered in the survey is categorized into one of four energy services: heating and cooling, lighting, domestic end-use appliances, and business-end use appliances (the assignment is provided in Appendix 1, table 1). The energy demand for the four energy services is calculated by totaling the energy services demand for all appliances categorized under each group. Similarly, the total household level energy services demand is the sum of energy services for all appliances owned by the household.<sup>16</sup> The household demand for energy services based on survey data is expected to be close to the electricity consumed by the household as observed in the billing data.<sup>17</sup> To check this relationship, the (log of) household demand for energy services per day from the survey is regressed on the (log of) perday electricity consumption observed in the billing data.<sup>18</sup> Results in Table 5 show that the household demand for energy services demand calculations based on appliance ownership and usage is a better predictor of households observed daily demand at levels more than 0.67 kWhs per day (~20 kWhs per month). Therefore, we expect our demand estimation models to fit better for customers above that threshold.

The data on household income and appliance ownership is based on self-reported household surveys. To the extent that the errors in these self-reported measures are independent of the latent true variable (i.e., classical measurement error), the instrumentation approach in GMM estimation allows us to obtain consistent point estimates of parameters of interest.

# 2.3 _Temperature and precipitation data_

The daily temperature and rainfall data are at a 1x1 degree and 0.25x0.25-degree gridded resolution from Srivastava, et. al (2009) and Pai, et al. (2014), respectively. The locations of the villages and census enumeration blocks are matched to these grids to obtain household-level measures of temperature and rainfall. Figure 9 shows deviations of daily temperature and rainfall in
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those found with labor market surveys, we confirm both that they are very high for poorer countries and that they decrease sharply with development, consistent with the literature. However, we also find that across a wide range of measures and functional forms, physical productivity elasticities are very low or negative in developing countries. These findings are consistent because while agglomeration appears to put downward pressure on firm mark-ups, marginal costs rise steeply with density. Hence, the higher wage and TFPR elasticities are largely driven by higher urban costs which, once adjusted for, leave no gains in efficiency of physical productivity with density. The adverse combination of very low efficiency and high marginal cost elasticities appears to diminish on both fronts with development - Chile, the richest country in our sample, shows both positive efficiency and negative cost elasticities. The finding of limited or negative physical productivity growth with urbanization, which we term “sterile agglomeration,” is likely due to the fact that developing world cities are often not being driven by structural transformation and positive agglomeration effects, but more likely by natural resource and aid rents, in-migration driven by a variety of push factors (e.g. poor basic services in rural areas), access to political power, and location near markets given poor transport infrastructure.

# **References**

Ackerberg, Daniel A, Kevin Caves, and Garth Frazer. 2015. “Identification Properties of Recent Production Function Estimators.” Econometrica 83 (6):2411–2451.

Ades, Alberto F.and Glaeser, Edward L. 1995. Trade and Circuses: Explaining Urban Giants. The Quarterly Journal of Economics, 110(1): 195-227 Amiti, M., O. Itskhoki, and J. Konings 2019. International shocks, variable markups, and domestic prices. The Review of Economic Studies.

Atkin, D., B. Faber, and M. Gonzalez-Navarro. 2018. “Retail Globalization and Household Welfare: Evidence from Mexico.” Journal of Political Economy 126 (1): 1–
, investment, domestic credit, formal sector employment, and the like. Such data was last captured for industrial and developing countries for the period 1978-91 in the World Bank’s Bureaucrats in Business Report (1995). Since then the 2000 World Development Indicators updated a few measures for a few selected countries, covering the period 1990-97. But spotty and relatively old data continue to make a systematic analysis difficult.

27. Nevertheless, anecdotal information shows that while privatization activity has grown, it appears at the aggregate level to be still small relative to the stock of state enterprises. Since 1990, the average number of transactions per country per year has been only five, compared to three in the earlier time period from 1980 to 1993 (World Bank 1995), and compared to a much larger number of enterprises that could have been divested in almost all countries. Moreover, most countries in fact fell short of the average given the concentration of transactions in a few countries. This suggests that after more than 20 years of privatization, government ownership is still prevalent in many developing regions and in certain sectors more or less across all regions of the world.
to measure it. The most widely used method, face-to-face interview, resulted in the lowest IPV rates, followed by the more private ACASI method, and finally, the anonymous list method with the highest. In Rwanda, IPV rates were double, and in Nigeria, up to 39 percent greater when measured using the list method compared to the direct methods. There was no statistically significant difference between the list and direct methods for rates of non-partner sexual violence in Rwanda or sexual IPV in Nigeria. This lack of difference for sexual violence could be explained by a lack of statistical power in Rwanda, and a Nigerian context where sexual IPV was not significantly stigmatized.

We also that misreporting is systematic and correlated with characteristics often theorized as causes of IPV and targeted in women’s empowerment programs. The characteristics that correlate differently with IPV depending on the measurement method used include indicators of women’s education and employment, community gender norms, vulnerability, and marital relationship quality. For example, in Rwanda, when IPV was measured using the direct methods, there was no relationship between IPV and conservative community gender norms. However, when IPV was measured using the list method,

> 1In more than 60 developing countries, Demographic and Health Surveys (DHS) have included a standardized domestic violence module where enumerators directly ask women about their experience of a set of specific emotional, physical, and sexual abuse episodes by an intimate partner. Using the face-to-face method, the WHO estimates that approximately one in three women worldwide has experienced IPV in her lifetime (García-Moreno et al., 2013). IPV prevalence varies widely, though rates are generally highest in Africa (Devries et al., 2013; Global Burden of Disease, 2017). Researchers collecting IPV survey data take steps to minimize the risk of underreporting, for example by instituting ethics protocols about privacy during interviews, asking multiple questions to give respondents several opportunities to disclose violence, and using specially-trained female enumerators to conduct women’s interviews (Heise and Hossain, 2017; WHO
16 A prominent example are the ANC data points from the WHS where a coding error caused the 2018 HEFPI version’s points to substantively understate the true rate of pregnancies with 4 or more ANC visits – in the case of Burkina Faso by over 14 percentage points.<sup>17</sup> In some cases, we discovered that the omission of a questionnaire’s filter questions or errors in the coding of missing values in raw survey data sets caused mistakes when we computed indicator values from micro-data. These errors were sometimes also committed by the survey reports – for instance, due to an erroneous coding of missings in the Côte d’Ivoire 2006 MICS immunization variables, the survey report and the 2018 HEFPI data point overstate the true full immunization rate by more than 15 percentage points. For the 2019 version of HEFPI, we have corrected such errors whenever we were confident that we could recover the true indicator values from the raw data.

Moreover, for the 2019 HEFPI database, we reviewed all MICS data points. We corrected coding errors, and, among other things, better aligned the definitions of “skilled birth attendants”, “modern contraceptives”, and “formal health care providers” (to treat child acute respiratory infections) with those used for points from DHS and other maternal and child health surveys.

In total, coding corrections led to changes of 10 percent or more relative to 2018 HEFPI indicator value levels for 152 data points for which sources remained unchanged. These data points

> 17 Another survey-family-wide correction in the 2019 HEFPI version is that we now follow the official DHS (StatCompiler) method to compute rates of stunting and underweight from DHS data by (a) using all children sampled in the anthropometry module and (b) applying anthropometry module sampling weights. For the 2018 database, we had limited our analysis to children whose mothers were present in the household and applied the ordinary DHS child sample weights. The changes make only small differences in practice, as all changes in rates of stunting
over 2020 and 2021 in 154 countries. The data draw on information from the Global Database on Social Protection Responses to COVID-19 (Gentilini et al. 2021), budget data from official documents (including IMF Article IV revisions and other international organizations’ related documents), government websites, and news sources. The key criterion for the inclusion is that the component of SP spending must have been designated as a response to the pandemic. In some cases these were new programs while others were expansions to existing programs. To the extent feasible, we estimated the budget corresponding to the expansion of an existing program (for example, the cost of extending the eligibility period of unemployment benefits) and not the whole pre-existing program, though we 15
also statistics published by countries’ central banks if possible. When multiple interest rate series are<sup>6</sup> available, we choose the one closest to the U.S. 3-month Treasury bill rate.<sup>5</sup> We select data series—and hence countries—based on the criterion that the series has no more than five missing values.<sup>7</sup> Among the 99 countries, we find 44 with sufficient data for all foreign variables, hence are allowed to be included in a “Full” VAR analysis described later. There are 34

> 3The specific series we use are: “Producer Price Index by Commodity: All Commodities”(PPIACO), “Consumer Price Index: Total All Items for the United States”(CPALTT01USM661S), “Industrial Production: Total Index”(INDPRO) and “Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States”(IRLTLT01USM156N). They are all downloaded from FRED St. Louis and adjusted for seasonality by Fed X-11 routine whenever needed. In computing CP, P and IP, we take the log of each raw series and multiply by 100.

> 4The measure integrates 0/1 indicators of controls in 32 transaction categories. Each of these indicators belongs to one of the 10 _asset_ categories, and represents restrictions either on inflows or outflows. The subset of indicators under each _asset_ category are then averaged to get two indicators - one for outflow, another for inflow. FKRSU then define the overall inflow (outflow) restrictions index - KCI (KCO) - as the average of the 10 inflow (outflow) indices. Our main KC measure is the average of KCI and KCO. We check robustness to use KCI or KCO, or on selected asset categories.

> 5We refer to the IFS World Notes to determine the exact maturities of each interest rate variable for each country. For instance, when there are no series explicitly defined as “3M Tbills