rafmacalaba/data-use-mentions-extended huggingface dataset:
.434|0.458|0.464|0.445|0.440|0.396|
|Theil T|0.327|0.35|0.396|0.47|0.474|0.410|0.399|0.302|
|R10/20|4.48|4.87|5.66|6.47|6.74|6.19|6.11|4.81|

Source: Calculations based on ENEU/ENET (third quarter).

5
**<mark>Table 7. P</mark> ercentage of Sub‐sample of Private Connections in Each Tariff Block** **<u>of the Water IBTs (Addis Ababa, 2016)</u>**

||**Wate**|**r**||
|---|---|---|---|
||||**Average price**|
|**Block No.**|**No. Observations**|**Percentage**|<br>**paid by households in**<br>**this block**|
|**1 [0 – 7 m3]**|182|50%|0.08|
|**2 [>7 – 20 m3]**|151|41%|0.18|
|**3 [>20 – 40 m3]**|28|8%|0.23|
|**4 [>40 – 100 m3]**|5|1%|0.29|
|**5 [>100 – 300 m3]**|0|||
|**6 [>300 – 500 m3]**|0|||
|**7 [>500 m3]**|0|||
|**Total**|366|100%||

# **_Estimates of the magnitude of electricity and water subsidies received by households with both private electricity and private water connections_**

The average monthly electricity use for the 366 households with private electricity and water connections was 276 kWh. The average monthly cost for the utility to provide such service was $25, yet customers paid a monthly electricity bill of about $6.50; hence their average monthly subsidy was $18.40, which amounted to some 4.5% of
_(Log of emissions in tons)_ <!-- Start of picture text --> ARE<br>SGP<br>KWT<br>SAU AUS USA<br>CANNLD<br>BEL<br>KAZ RUS HKG<br>KOR<br>ZAF CZEISRGRCJPNDEUGBR<br>AUT<br>UKR IRNBLRVEN POL ITAESPFRA<br>MYS<br>UZB CHN ROMARGMEX<br>THA TURCHL<br>IRQ DZA<br>BRA<br>IDN EGY<br>COL<br>IND MAR<br>VNM<br>PAK PHL<br>NGA<br>7 8 9 10 11<br>Log of GDP (PPP) per capita, 2008<br>4<br>3<br>2<br>1<br>Log of emissions (in tons) per capita, 2008 0<br><!-- End of picture text --> _Source: Authors’ calculations, based on data from the World Bank’s World Development Indicators._ Any commitments by developing countries to reduce emissions will lead to an increase in the price of energy and hence implicitly in the price of carbon, which is embodied in fossil fuel-based energy products. This price rise will affect not just the overall economy but also the composition of production and the distribution of consumption. On the production side, manufacturing tends to be far more energy- and carbon-intensive than agriculture and services, so any increase in the carbon price is likely to lead to a contraction of manufacturing. In India, for example, the carbon intensity of manufacturing was about 518 tons per million U.S. dollars in 2004, much more than the 301 tons in agriculture and 231 tons in services. Of course, there are big differences within manufacturing, with certain energy-intensive manufacturing sectors emitting more than twice as much carbon as others.

In an international system of trading emissions rights, developing countries might have to cope with even higher carbon
|Bangladesh<sup>b</sup>|13.6|2.8|41.3|4.7|3,319|
|---|---|---|---|---|---|
|Mali|11.1|2.2|29.6|0.9|1,971|
|Uganda|8.7|1.8|29.1|0.9|1,687|
|Ethiopia|19.8|3.5|63.9|2.4|1,608|
|Niger|9.1|0.8|49.7|0.1|914|

_Source:_ Original calculations using data from several household surveys. _Note:_ These are countrywide figures. Poor households are defined as belonging to the first four deciles of the expenditure (or income) distribution in each country. All figures are calculated using sample weights. Water connection is derived from questions about main source of drinking water—but some countries have information about sources of water for other uses (like Bangladesh), which are then also used to construct water connection. More information can be found in the appendix. Column 5 refers to GDP per capita, PPP (constant 2011 international U.S. dollars). GDP = gross domestic product; PPP = purchasing power parity.

a The figures for rural Vietnam, using the 2015 Vietnam Household Registration Survey (VHRS), are much higher than the figures presented in JMP, using the Household Living Standards Survey (HLSS) 2012 that shows a rural rate of 13 percent. This is likely due to differences in the regional coverage of each survey. The VHRS 2015 has a sample that is representative of the population in five provinces—Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong, and Dak Nong— while the HLSS 2012 is supposed to be nationally representative.
Table 6, panel B, presents the results on profitability for the entire sample and separately for farms with areas that are less than or greater than 120 ha, highlighting two points. First, the area cultivated remained approximately constant, consistent with the results from the remotely sensed data. Physical output for key crops decreased by between 6% and 21% for rapeseed and maize or sunflower, respectively, in the first year of the war but was only marginally (between 1% and 6%) below 2021 levels in 2023. These results support the focus on area rather than yield in our earlier analysis.

Second, revenue per hectare decreased by 34% between 2021 and 2022, from US$1,088 to US$714, with a further decline to US$597 (or 56% of the 2021 level) in 2023. Combined with an increase of 26% in input costs over 2021 levels in 2022, this implied a drop in profit per hectare by 60%, from US$778 to US$313. The share of output sold to the market decreased from 78% to 68% in 2022. While the level of market participation recovered to the original level in 2023, the share of output sold in 2023 remained at 62%, 20 percentage points below the share attained in 2021. Although lower input prices implied a small recovery of profits to US$397/ha, this was still only 54% of the 2021 level.

# **4.2 Farm-level analysis and variation of impacts by farm size group**

The farm survey panel data can be used to run farm-level fixed effect regressions for the level of market participation, output value, input cost, and profits overall and by farm size group. In the absence of other confounding factors, year dummies for 2022 or 2023 can be interpreted as conflict impact and the difference between the two years as the result of farmers’ adaptation overall or by farm size group. Columns 1, 3, 5, and 7
Panel B and Panel C). This lack of improvement is not surprising: we have repeatedly shown that the government continues to play a very important role in interacting with firms.

Larger firms face lighter government expropriation on average, and so do politically connected firms (i.e., Party membership for firm owners)—the sign tends to be consistent, though the coefficient is often insignificant (see Table 3). Interestingly, female-headed firms experience higher fee/sales, and the coefficients on other expropriation outcomes are also positive though insignificant. This is consistent with an emerging literature from firm-level and cross-country evidence that women are less involved in and more ill-adapted to corruption (Dollar et al. 2001; Swamy et al. 2001; Hanousek et al. 2017).

# **Evolution of entrepreneurs and firm characteristics**

Here we rely on the nation-wide private enterprise survey data on small- and medium-sized firms, and the CSMAR data on listed (and thus large) firms.

_Evolution of entrepreneur characteristics_ **.** On average, Chinese private entrepreneurs are 43 year old. The age profile remains stable over time. The percentage of female entrepreneurs has risen steadily from 11% in 1991 to 17% in 2012, indicating declining disadvantage for women to run businesses. Most significantly, the education of private entrepreneurs has improved significantly, from fewer than 9 years in 1991 to around 15 years in 2012. According to panel A of Table 4, only 5.2% of private entrepreneurs finished college in 1991, whereas 65% of them did so in 2012. The education levels of private entrepreneurs in larger firms are higher and have improved faster than those in smaller firms (see panel B of table 4). On average, the number of years of schooling is higher by 1.5 years among the top-quartile-asset firms than among those in the bottom-quartile-asset firms. The fast improvement in the education level of entrepreneurs indicates that Chinese talent allocation is increasingly shifted to the productive private sector, mostly from the
PhD as well as field, university, country and nationality of professors are available from various data sources published by the NSF at the national and state levels. Birthplace of the professors are available from the faculty information of the various universities by schools and departments. In addition, stay rates of international students have been published by the Oak Ridge National Labs (Finn, 1999-2014). Such details are not available for other countries, but the UK, Australia, Turkey and Canada publish data on international students by country and fields of study such as engineering. Other countries such as New Zealand, Ireland and Japan and a number of EU countries also have some detailed data on international students.

# _(ii) Application of International Trade Theories to Trade in Education Services_

Global student mobility is an international trade in services phenomenon. The student sending country imports services, while the receiving country receives tuition and other expenses from the student and hence is the exporting country. Despite setbacks during the early post-9/11 period (2001-2006), the number of international students in the US has generally trended upwards (Bird and Turner, 2014; Niemen and Swagel, 2009; Alberts, 2007). In addition, for the host country national and sub-national 16
-1.00|3.74|6.28|
|Distance to branch (km)|3.70|1.71|593|1.27|1.63|3.60|6.34|7.10|
|Hyperbolic|0.23|0.42|593|0|0|0|1|1|
|Patient now, impatient later|0.27|0.45|593|0|0|0|1|1|
|Impatience (switching point, out of 6)|2.90|2.03|593|1|1|2|6|6|
||Panel B:|Total of it|emize|d expenditur|es from roun|d 1 surve|y||
|Total|11,500|15,860|593|520|960|5,625|28,950|43,760|
|Food|4,593|5,377|593|220|480|2,780|10,710|16,386|
|non-durables|6,942|12,456|593|100|280|2,440|17,000|32,180|
|Unplanned food|422|815|593|0|0|60|1,300|2,130|
|Unplanned non-durables|114|428|593|0|0|0|200|700|
||Panel C:|Bank tran|sactio|n before rou|nd 1 survey||||
|Any activity 7 days prior|0.07|0.26|593|0|0|0|0|1|
|Any activity 90 days prior|0.33|0.47|593|0|0|0|1|1|
|Value of deposits 90 days prior|13,045|107,024|593|0
potential effects.<sup>1</sup> Another strand or work connected to ours explored PTAs and their potential effects.<sup>2</sup> . Third, recent papers have explored the trade effects of subsidies, including those by the WorldBank (2023) and Rotunno and Ruta (2024). Finally, studies have investigated the interconnections between industrial and trade policies. Lashkaripour and Lugovskyy (2023) show that when unilateral adoption of corrective industrial policies is associated with trade barriers, countries risk _immiserizing growth_ and a global race to the bottom. Ju et al. (2024) provide a quantitative evaluation of the US-China trade and industrial policy competitions.

The rest of the paper is structured as follows: Section 2 illustrates the data and the empirical specification for the analysis. Section 3 illustrates the main findings. Finally, Section 4 concludes.

# **2 Data and Empirical Strategy**

## **2.1 Data**

We use three different datasets. The dependent variable consists of bilateral, product-level (HS 6-digit) trade data from Comtrade-BACI Gaulier and Zignago (2010), covering the period 2012-2022.<sup>3</sup> There are two key explanatory variables: a measure of new industrial policy announcements as catalogued by the Global Trade Alert database; and measures of preferential trade agreement membership and depth, as documented in the World Bank’s Deep Trade Agreements Database (Mattoo et al., 2020).

### **2.1.1 Global Trade Alert Database**

Established in response to the 2008 financial crisis, the Global Trade Alert (GTA) is the only comprehensive database and independent monitoring platform that provides timely information on state measures likely to affect world trade. The database includes measures that are traditionally considered protective, such as tariffs and quotas, as well as subsidies and other mechanisms that governments use to support domestic industries (see Figure 4). The database also classifies the measures into “restrictive” (red),
Poverty Gap|14.5%|6.9%|8.6%|

Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal. Notes: The moderate poverty line used is half a minimum wage of 2019 in Brazil: BRL 499, while the extreme poverty line is BRL 178.

# 3.3 Concentration of taxes and transfers

Two aspects stand out regarding the concentration of fiscal interventions in Brazil: direct transfers and direct taxes. Half of the direct transfers go to the first income quintile: this group includes the population in extreme poverty and part of those in moderate poverty (Figure 5). This could be a reason for the reduction in extreme poverty being higher than that in moderate poverty. We also observe that the richest income quintile pays almost all direct taxes and the proportion of indirect taxes paid by the highest income quintile is close to their share of market income plus pensions. Meanwhile, the share of direct transfers, education, and health is higher than the participation in market income plus pensions for the first three quintiles. When we consider education in-kind transfers, it is higher for the first quintile and gradually decreases until the fifth quintile, which receives about 12% of total education expenditures. The distribution of health benefits, in turn, is similar to that of education but with a slightly lower percentage for the bottom 40% of the population and slightly higher for the top 40%.

22
route start/end points using population weights.<sup>8</sup> To approximate this, I compute a global weighted average across routes with weights equal to the product of start/end cell populations divided by their spherical distance.<sup>9</sup> This yields NS of 64.2, RE of 0.68, and TE of 43.9, reflecting greater efficiency on longer routes (Wolfram, 2021). But even the most favorable RE estimate of 0.68 is far below Wolfram (2021)’s estimates of 0.843/0.767 for US/European road networks.

## **2.3 Market Access**

Whereas route and time efficiency are important from a transportation standpoint, the spatial economics literature such as Donaldson & Hornbeck (2016), Jedwab & Storeygard (2022) or Peng & Chen (2021) has used market access (MA), typically defined as an inverse-distance weighted sum of population, or a proxy therefore such as built-up in the case of Peng & Chen (2021), to evaluate economic gains from transport investments. The MA measure for cell _i_ is where _Lj_ is a measure of population and _τ_ a measure of distance, travel time, or transport cost, weighted by an elasticity _θ_ measuring how trade volumes fall as distance/time/costs increase. The value of _θ_ is contentious, for example Jedwab & Storeygard (2022) use _θ_ = 3 _._ 8 whereas Peng & Chen (2021) use _θ_ = 1. In both cases, _τij_ measures travel time.

To appropriately measure continental MA implied by the road network, I incorporate spatial differences in purchasing power into _Lj_ . Kummu et al. (2018) provide a spatial estimate of GDP in 2015 USD PPP terms by distributing national GDP estimates from the World Development Indicators at 5 arc-min (0.0833 degrees or 9.3km at the equator) resolution using subnational value-added estimates from Gennaioli et al. (2013
physical accessibility challenges in many of the provinces, a remarkably high share of women receives antenatal care and delivers in health facilities in the DRC. According to the latest estimates, 88 percent of women received at least some antenatal care, and 80 percent delivered at health facilities. However, several challenges remain: only 17 percent of women started receiving antenatal care in the first trimester, and only 48 percent received the recommended number of at least four consultations (Demographic and Health Survey 2013/14).

# 3. Data

Our data were collected in 133 health zones in 14 provinces between June 2015 and March 2016. The sample and geographical scope were purposively chosen for the evaluation of two performance‐based 5
interested in the role of IPRs in attracting trade flows and not in creating trade flows, we only use the Park and Ginarte index of the destination country of the trade flow as explanatory variable (that is countryj in the case of exports and country i in the case of imports).

# **IV. Empirical Estimates**

Our estimation results are presented in Tables 2 through _5._ The overall performance of the rnodels is quite good. Most gravity variables have the expected signs and are statistically significant. Exceptions are for total non-fuel trade (Tables 2 and 3) the coefficient on the border dummy which has the wrong sign in the probit equation and is not statistically significant in the gravity equation; and the wrong signs of the coefficients on the dummies indicating EC and EFTA membership in the gravity equation, which are, however, never significant. For the high technology aggregate (Table 4 and 5), the exceptions are similar: the coefficients on the border dummy in the probit equation and on EC and EFTA membership in the gravity equation are never statistically significant and sometimes have the wrong sign. Likelihood ratio tests indicate that for all alternative specifications estimated the explanatory variables are join,tly significantly different from zero.

The estimated correlation coefficients between the probit and gravity equations p are always close to zero and not statistically significant based on a likelihood ratio test for both total non-fuel and high technology trade. This suggests that for our data it would have been possible to estimate the equations independently and that the exclusion of zero observations in the gravity equation does not lead to a bias stemming from a non-random sample selection.

Recalling the theoretical ambiguity of the effect of IPRs on bilateral trade described in Section II, we had no prior expectations regarding the sign of the coefficient on the Park and Ginarte index. F'or both total non-fuel imports and exports, the Park and Ginarte index has only a small effect on the probability of positive trade flows between countries, although the effect is positive and statistically significant at the 5
Earnings?” _The Quarterly Journal of Economics_ 106(4): 979‐1014.

Anker, R. 1983. “Female Labour Force Participation in Developing Countries: A Critique of Current Definitions and Data Collection Methods.” _International Labour Review_ 122(6):709‐724.

Bardasi, Elena, Kathleen Beegle, Andrew Dillon, and Pieter Serneels. 2011. “Do Labor Statistics Depend on How and to Whom the Questions Are Asked? Results from a Survey Experiment in Tanzania.” _World Bank Economic Review_ 25(3): 418‐447.

> 37 While the instrument reflects an exogenous policy change that extended primary school from 4 to 7 years in the mid 1960s, it is unclear to what extent it is applicable to the entire sample of analysis.  As the change in policy took place 40 years ago, it had an immediate impact on those who were 10‐13 years old in the mid 60s, or 50‐53 in the mid 2000s. With average age of 38, this is likely to be a minority in the IFLS sample.  Because the change in policy took place so long ago, we do not consider this as identifying variable for our control function estimates. 38 Kenya, Nigeria, Ghana, Burkina Faso, and Cote d’Ivoire

21
Figures in parenthesis indicate the years the data refer to, while figures in brackets are percentages of the labor force. The fonnal private sector may include state-owned commercial firms in some countries. Data on the labor force for all countries, except Senegal, are from the International Labour Office. Data on private sector employment in C6te d'Ivoire are from the _Banque de Donnees Financieres._ In Senegal, they are from _Banque de Donnees Economiques et Financieres._ Other data for Senegal are from the Ministry of Finance. For other countries, data on formal sector employment are from the _Statistiques Economiques_ of BCEAO.

4
.971|0.165|0|1|2853|Yes|
|(12) Staff attitude<br>1 _♦_§|4|3.752|0.555|1|4|2845|Yes|

†: dummy variable; §: likert scale (1-4); _¶_ : likert scale (1-5); _♦_ : measured across all health episode types (ante and post natal care, child birth, under-2 vaccinations, and illness and injury); w , 1 , 6 , and 12 stand for measures referring to the last week, one month, six months and twelve months respectively.

**Table B.7:** Community Support

||Median|Mean|SD|Min|Max|N|BL data|
|---|---|---|---|---|---|---|---|
|(1) Contributions to clinic (community survey)<br>6|0.083|0.075|0.934|_−_1.170|4.193|508|Yes|
|(2) Contributions to clinic (facility survey)<br>6|0.317|_−_0.051|1.125|_−_5.373|1.503|508|Yes|

In the community survey, we ask about meetings between the clinic and community as well as labor or financial contributions to the clinic. In the clinic survey, we ask about labor contributions as well as disputes between the community and the clinic. †: dummy variable; §: likert scale (1-4); _¶_ : likert scale (1-5); _♦_ : measured across all health episode types (ante and post natal care, child birth, under-2 vaccinations, and illness and injury); w , 1 , 6 , and 12 stand for measures referring
are economies of scale in aid management systems, their quality may tend to be higher in larger countries. For a

> 6 An index scored from 1 to 7 is available going back to 1972, but we use a more finely-grained version scored from 0-100 that is available for 2003 onward.  The political freedoms indicator also produced by Freedom House is correlated with civil liberties at .95, and results are similar if it is used instead.

> 7 Anecdotal evidence from country evaluations suggests that donors sometimes bypass country systems because they are “slower and more cumbersome” as well as vulnerable to corruption (Wood et al., 2011).

10
observations from LLECE, 37 from SACMEQ, and 30 from PASEC.  Six country‐year observations from LLECE and one observation from PASEC are dropped because the occur in country‐years for which an international test is available.

- <u>Early Grade Reading Assessment (EGRA).  The HLO database contains data from 75 EGRA</u> assessments carried out in 48 low‐ and middle‐income countries.   There are 27 countries where EGRA is the only available international test, and so for these countries the HLO simply is based on the EGRA assessment.  The remaining 22 countries also have data from one of the major international and/or regional testing programs.  For these countries, EGRA data are included in the time series of test scores for the country only if (a) the additional test substantially expanded the time series coverage of the combined HLO, and (b) the additional test did not

30