rafmacalaba/data-use-mentions-extended huggingface dataset:
answer for a typical household in a typical village, not to self-assess their own temptations. The survey was conducted online in June-July 2016. Across the 43 responses that we received, we assign each item its average score on the 5-point scale, and classify the top third (6 items) as the temptation goods in the study. These are: sugar, rice, cooking oil, soap, cigarettes, and sweet bananas.<sup>19</sup> For this analysis, household fixed effects are identified, so we can examine a specific behavior associated with not buying in bulk, rather than base tests on variation in losses. The primary outcome of interest is _Khi_ , the number of purchases of item _i_ by household _h_ . We first examine whether households make fewer purchases when bulk discounts are present, a response that indicates at least some attending to bulk discounts. We then test the hypothesis that households ration their stocks of temptation goods by purchasing them more frequently than non-temptation goods. Finally, we estimate the joint effects of temptingness and bulk discounts on _Khi_ , and test for differential responses between high- and low-loss households. Identification in these regressions is from three sources: household fixed effects control for average purchasing behavior; the temptation survey provides an objective, exogenous measure of the temptingness of a good; and the fact that households face some flat and some non-flat unit price schedules in their local markets provides exogenous withinhousehold variation in the presence of bulk discounts.<sup>20</sup> We include item fixed effects, when Regression results are in Table 9. Column 1 shows that after controlling for both household and item fixed effects, the average effect of a bulk discount is to induce fewer purchases. The average household does react to bulk discounts, to some degree. Column 2 shows that this result does not hold without the item fixed effects. Households also react to the temptingess of the good, but in the opposite direction (column 3). Households make 0.65 more purchases on
.711)|
|N. Obs|3,098|3,098|1,092|1,091|2,079|2,079|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|No|Yes|No|Yes|No|Yes|
|Sample|All Years|All Years|1970-99|1970-99|1999-2018|1999-2018|

**Notes:** This table reports a set of panel data regressions where the dependent variable is the average growth rate of real annual GDP per capita over a 10-year period and the explanatory variables are the lagged values of: net FDI inflows as a percentage of GDP; credit to the private sector by deposit money banks as a percentage of GDP (this variables is scaled by 100); the log of average years of secondary schooling in adult population; the log of initial GDP per capita; the log of inflation; the log of export plus import over GDP; the log of government expenditure over GDP; and the log of the black market premium. The first 10-year panel starts in 1970, and we include data up to 2018. Columns 1, 3, and 5 include year fixed effects, columns 2, 4, and 6 include country and year fixed effects. Robust standard errors double clustered at the country and year level are in parentheses. Significance levels are denoted as: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Figure 6 reports the coefficient estimates for the interaction terms themselves. Panels a and b show the coefficients for the interaction between FDI and credit. These are never statistically significant when only year fixed effects are included. However, they are significant in the early estimation windows when we include both country and year fixed effects (panel b).<sup>9</sup> When we look at the interaction between FDI and schooling
rate for their samnple of 98 non-oil producing countries to be around 0.5. Finally, including all five institutional quality variables and restricting their coefficients to be the same improves the regression fit (see equation 6).

At this stage we foresee arguments against using contemporaneous values of institutional quality measures to explain the variations in the industrial growth. This is the same as saying that industrial growth in turn may be causing improvements in the underlying institutional quality measures. Although we formally account for possible endogeneity of these variables below, we dismiss these arguments (at least partially) based on the fact that (albeit correlated with general economic growth) industrial growth alone is unlikely to generate major changes in the legal system, contract enforcement and level of corruption. To check for possible endogeneity in the institutional quality variables, a robustness test of the results with respect to the (timing of the) institutional variables was conducted. We did so by including initial (1982) values of PRS indicators in the above specification, which does not change the qualitative importance of the results and changes the magnitudes and significance pattern of the coefficients only marginally".

To check the robustness of our results with respect to different measures of Institutional Quality, we introduced yet another measure of institutional quality - the Contract-Intensive Money ratio. Introduced by Clague _et al._ (1995) as the ratio of noncash balances within M2<sup>2</sup> , the measure intends to quantify contract enforcement and property rights. We report these results in Table 4. They appear to go along the lines of " The results of those regressions are available from the authors upon request.

12 The ratio is defined as (M2-Cash)/M2. Cash (Currency in circulation outside of banks) is line 14a of IMF's International Financial Statistics. M2 is the summation of line 34 (Money) and line 35 (Quasi-Money) of IMF's International Financial Statistics.

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finance is not the only hurdle that women encounter when starting and growing a business. Campos et al. (2019) identify a number of constraints that female entrepreneurs in Sub-Saharan Africa face in the realm of endowments as well as at the household level. These include differences in skills, capital, networks, occupational opportunities, and safety. These underlying constraints shape women’s strategic decisions, resulting in them being more likely than men to end up with smaller firms in lower productivity sectors. Regulatory constraints are among some of the more prominent contextual-level hurdles that women face. For example, the 2020 Doing Business assessment shows that additional steps are often required for women to start a business compared to men and that it takes on average 35 days for a woman to start a business compared to 34 days for men. This is considerably longer than the 22-day average for the Sub-Saharan Africa region (World Bank 2020c). The previously referenced study highlights the personal characteristics of the businesswomen that appear to have helped them succeed as well as some of the structural factors that, according to them, have inhibited them from achieving greater success in starting and managing their businesses. In general, the women were younger when they started their businesses (50 percent were between the ages of 36 and 45), 62 percent came from families who had experience of owning and managing businesses, the women were highly educated (62 percent had a university or a post-graduate degree), and 67 percent had professional experience in the formal sector (Musa 2012). Also of interest is the fact that, in contrast to a number of the larger businesses in Sudan

> 22 (World Bank 2014b) It is noteworthy that while women identified access to finance as a major obstacle, more female-led firms had a bank loan/line of credit than male-led firms (13 and 4 percent, respectively). Other differences in the characteristics of firms with a woman versus a man as the top manager include female-led firms outperforming male-led firms on having an internationally recognized quality certification
surroundings back and restacking it vertically on its source pixels at city centers (Abrahams et al., 2016).

Deblurred DSMP-OLS RC annual composites for the years 1996, 2000 and 2010 were previously inter-calibrated and corrected for a multi-temporal analysis of urban form and city productivity in Latin America (Duque et al., 2017). In that work, the three nighttime images were used to delineate urban extents in each year for most of the Latin American and Caribbean cities that had more than 50,000 people in 2010. We used those delineated urban extents for the year 2010 to identify the metropolitan areas in the region. We consider the presence of a metropolitan area when more than one municipality or equivalent administrative unit intersects a single urban extent with more than 500,000 people in 2010. We use the administrative unit boundaries from the World Bank Latin American and the Caribbean Spatial Framework Database (Branson et al., 2016) for this purpose. Metropolitan area boundaries were obtained by aggregation of all of the administrative units that intersected the same urban extent. We verified each obtained metropolitan area with ancillary information from official sources to include those municipalities that are part of the official metropolitan area denomination but were not intersected by the urban extent. Figure 2 presents some
where the theory predicts that an increase in money supply, expected inflation, the exchange rate and foreign prices will all drive prices up, while an increase in real income will lead to a decline in the inflation rate. The effect of sluggish adjustment due to rigidities and inertia can be captured by adding the effect of lagged prices to the equation.

Tables 5, 6, and 7 present the results of the empirical counterpart to our theoretically derived equation by using monthly data for the period 1993:01-1997:09. Our empirical approach is divided into two parts. First, we employ Engle-Granger (1987) and Johansen and Julelius (1990) co-integration analyses in order to determine the existence of the long-run relationship between, P, e, y, and M.Y2 It is widely believed that variables hypothesized to be linked by some theoretical economic relationship should not diverge from each other in the long-run. Although such variables may drift apart in the short-run, they converge toward an equilibrium in the long-run, thanks to disequlibrium forces. Co-integration is viewed as the statistical expression of the nature of such longrun equilibrium relationships.

The empirical results indicate that there is a long-run equilibrium relationship between P and e, y, and M. The findings also show that, in the long run, inflation in Albania is positively related to money supply, and the exchange rate (USD), while it is negatively related to real income, which is proxied by the power consumption (PC)."<sup>3</sup> "<sup>2</sup> We have employed both the ADF test and Phillips-Perron test to investigate the time series properties of the variables involved. Considering the length of our sample, unit root testing with standard critical values could be misleading. Nonetheless, based on visual inspection and the results of these tests, it is safe to conclude that all the variables are integrated order of 1, 1(1).

'3 As widely acknowledged, there is a close link between aggregate
and García-Pérez, 2014). Other than being morally objectionable, affording individuals of different race and gender different opportunities may lead to a sub-optimal allocation of talent and a wasting of human resources. These inefficiencies are arguably harmful for growth.

The IPUMS USA database is also used to construct selected control variables. Following van der Weide and Milanovic (2014) we compute: (i) the percentage with a graduate degree among individuals between the age of 21 and 39 (Edu_ms); (ii) the share of women between the age of 24 and 65 who are out of the labor force (Olf_fem); and (iii) the population shares aged 15 or younger (Aged 0-15) and aged 65 or older (Aged 65+). We expand the number of controls by also considering a selected set of the independent variables used in Marrero and Rodríguez (2013), which include: (iv) the share of (nonagricultural) employment in construction (Emp_cons), finance, insurance and real estate (Emp_finan), and government (Emp_gov); (v) the percentage change in nonagricultural employment over the preceding decade (Emp_growth); (vi) the fertility rate measured as the number of live births per 1,000 women 15-44 years old (Fertility); and (vii) public welfare expenditure as a percentage of state personal income (Welfare).<sup>11</sup>

# **3.2. A first look at the data**

The four panels of Figure 1 plot the time-trends of the income growth rates (topleft, top-right and bottom-left panels) for the poor (10<sup>th</sup> percentile), middle-class (50<sup>th</sup> percentile) and the rich (99<sup>th</sup> percentile) as well as the time-trend of total income inequality (bottom-right panel) over the 50-year period. Low- and middle-income households have experienced a visibly different trend in income growth rates
as a liability of the guarantor sovereign. For example, the IMM lists guaranteed bonds in the section of the guaranteeing nation, for example, Great Britain.

- Finally, we exclude three sets of bonds: 10 bonds with erratic and infrequent price quotes (this includes a bond issued by the Confederate States of America in London)<sup>1</sup> ; 11 exotic bonds such as land warrant bonds or bonds issued by the Khedive of Egypt, but not by Egypt itself; and 37 bonds for which we did not find basic details on coupon, amount issued, or currency.

Our cleaned, <u>final historical dataset after these adjustments covers 209,890 monthly prices of 911</u> sovereign external bonds issued in US dollar and British pound.

# **Modern bond period, 1990-2016**

For the modern period, we use pricing data provided by JP Morgan, which is available from 1989 onwards, albeit initially for less than five countries that issued bonds in their Brady debt restructurings. More details are provided in the draft. In total the EMBI data cover 56,244 monthly price quotations of 6411 sovereign external bonds issued in US dollar (none is issued in British pound).

> 1 The so-called Confederate cotton bond was a 20-year security with a 7% coupon payable in British sterling and issued in London. The bonds were illiquid and appear only in our pricing database for a few months.

A6
3.3|
|(5) Quality‐adjusted<br>years of schooling<br>based on test score<br>gains acrossyears|Kaarsen (2017)|2.1|13.3|6.3|
|(6) “Human capital”<br>based on linear<br>combination of years<br>of schooling and test<br>scores|Hanusheck,<br>Ruhose, and<br>Woessman (2015)|1.3|2.2|1.7|

Source: Authors’ analysis based on Barro and Lee (2013), TIMSS 2015 data, Schoellman (2012), Kaarsen (2017), and Hanushek, Ruhose, and Woessman (2017). See text for details on how each measure is implemented.

Note: The sample of countries is the same 35 countries as that for Figure 2.1, except in the case of Row (4). Under the Schoellman approach, data are not available for 8 of those countries, and the analysis underlying Row (4) therefore only includes 27 countries.

These various approaches clearly have different implications for how one assesses 41
**Figure 5. Performance and Poverty and Inequality Impacts of the MSA Scheme under Baseline Scenario (prereform)**

<!-- Start of picture text --> a. Coverage (% of households in each decile) b. Benefit incidence (% benefits allocated to each decile)<br><!-- End of picture text --> <!-- Start of picture text --> 11.5 1<br>2<br>3<br>4<br>5<br> 1 2 3 4 5 6 7 8 9 10 100.0<br>c. Generosity (benefits as % of MYPP) d. Inequality, poverty, and poverty gap impact<br>4.4<br>Marginal Contributions to<br>0.00<br>Inequality<br>Change in Poverty Gap 0.11<br>Marginal Contributions to<br>0.01<br>Poverty<br>0.00 0.05 0.10 0.15<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by taking the difference between the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**Conversely, although effective for a limited period each year (five months), the heating allowance exhibits more extensive coverage than the Monthly Social Allowance (MSA).** The coverage of the heating allowance extends to households in the fifth decile, with more than half of the benefits going to the first decile and less than one-third being directed to those in the second decile (as indicated
research was supported by funding from 3ie, the Center for Economic Policy Research, the International Growth Center, USAID, the Abdul Latif Jameel Poverty Action Lab, the University of Virginia, the University of British Columbia, New York University Abu Dhabi, and the World Bank Jobs Umbrella Trust Fund which is supported by the Department for International Development/UK AID, and the Governments of Norway, Germany, Austria, the Austrian Development Agency, and the Swedish International Development Cooperation Agency. This paper was partly written while Mbiti was visiting the Department of Economics and the Population Studies and Training Center at Brown University. The research protocols were reviewed and approved by the Institutional Review Boards of Innovations for Poverty Action, Southern Methodist University, NYU-Abu Dhabi, the University of British Columbia, and the University of Virginia. The project is registered under the AEA RCT Registry (AEARCTR-0001948). The views expressed in this paper are those of the authors and do not necessarily reflect the the position of any agency or funder.

- NYU- Abu Dhabi; morgan.hardy@nyu.edu

> _‡_ University of Virginia, J-PAL, IZA; imbiti@virginia.edu

- _§_ University of British Columbia; jamie.mccasland@ubc.ca

- New York University; isabelle.salcher@nyu.edu
(Kroodsma et al., 2018). This measure likely underestimates the number of subsidy days, but by much less than if we applied the MOA’s minimum VMS ping rate to the AIS data (Figure A1). AIS transponders transmit similar data to terrestrial receivers and satellites as VMS transponders, but they have worse reception than VMS. We calculate that the fuel subsidy for the median vessel-year is $223,380 (2022 USD) using this measure of subsidy days, but because our measure is an underestimate, the median vessel-year annual fuel subsidy is likely greater. This subsidy amounts to about 10% of annual variable costs for the median vessel-year in our data (Sala et al., 2018). This underestimate of subsidy days affects our elasticity of fishing with respect to fuel subsidy received, but it does not affect our estimate of the effect of the fuel subsidy policy on fishing.

# **3 Data**

By combining publicly-available data from international organizations and Chinese agencies, we identify and collect the characteristics of subsidized Chinese distant water fishing vessels. Then we match these characteristics with fishing activity data. The resulting panel data are at the level of vessel-month-year.

We begin by collecting data from the seven Regional Fishery Management Organizations (RFMOs) that include vessel registrations from mainland China (Appendix B). For each RFMO-vessel, we recorded the vessel’s registry name and vessel characteristics, including gear, length, gross tonnage, engine power, and the year the vessel was built. Gross tonnage is a non-linear measure of a vessel’s internal volume. When vessels have the same name in Chinese pinyin, but the Chinese characters name and other characteristics are different, we regard them as different vessels. In most RFMOs, vessels reported additional identification information such as previous name, Maritime Mobile Service Identity (MMSI), call sign, and International Maritime Organization (IMO) number. We also gathered data from RFMO websites on the species vessels target when they fish, vessels’ authorized fishing area
# **References**

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%. Consistent with Figure 1, the three panels of Figure 3 show that fires occur mostly in the dry season (November-February), and especially so in November and December, the first two post-harvest months. The panels also show that the mean number of forest fire occurrences is similar in the (then yet to be) treated forests and in the 65 other classified forests (depicted by the continuous and dashed lines, respectively). Also note that the variance around these means is quite substantial (as indicated by the

> 4 We preferred MODIS over NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) as the latter data are only available from 2012 onwards. Hence, using FIRMS would not allow us to properly train the SCM because of the paucity of pre-intervention observations.

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