rafmacalaba/data-use-mentions-extended huggingface dataset:
14 Figure 4: Change in Vulnerability between 2009 and 2011 by Oil Trade Status <!-- Start of picture text --> 70 66 50<br>45<br>Net importers 45 Net exporters<br>60<br>40<br>50 35<br>30<br>40<br>25 23<br>20<br>30 24 20<br>20 15<br>10 8<br>5<br>10 3 5 2 1 5<br>0 0<br>Below 0 0–2.5 2.5–5 5–7.5 7.5–10 Above 10 Below -10 -10 to -5 -5 to 0 0 to 5 Above 5%<br>Vulnerability as % of GDP Vulnerability as % of GDP<br>% of countries<br><!-- End of picture text --> _Source_ : Author’s calculations based on data from EIA (2013b).

# **4. Changes in End-User Oil Product Prices**

This study collected information on retail prices of gasoline, diesel, kerosene, and LPG in January 2012, July 2012, and January 2013, and calculated the degree of pass-through to consumers of world price increases since January 2009. Because prices at different stages in the supply chain are not available for most developing countries, a simplified methodology, described in the next paragraph, is used, yielding meaningful results only if the price difference between the two time periods is large. January 2009 was selected as the initial period for two reasons: first, the oil product prices in January 2009 were as low as in the latter half of 2004, and second, intervals of three to four years were deemed long enough to provide sufficient time to adjust to world price movements.

The pass-through coefficient for each fuel is based on the ratio of the difference in domestic retail prices to the difference in free-on-board (FOB) prices of
amounts paid out by transfer programs available from the BEA. The transfer amounts are logged and these programs include social security, other retirement and disability insurance transfers, Medicare, Medicaid and other vendor payments, military medical insurance, SSI, EITC, other income maintenance programs such as TANF and WIC, state unemployment insurance, other unemployment insurance, Veterans’ benefits, education and training assistance, other government transfers, transfers from nonprofit institutions, and transfers from businesses. Measures of grocery industry concentration, which include the number of establishments per population and the HHI, are obtained from the County Business Patterns (CBP) made available by the Census Bureau. The HHI is calculated using the mid-point of available employment size brackets.

# **G Graphical Evidence**

We present some graphical evidence that suggests the identifying assumption of parallel trends is satisfied as an alternative graphical illustration of parallel pre-trends. Appendix Figure O3 plots the revenue-weighted average grocery store price indices by the quantile of change in residualized log synthetic benefits per population, our IV, for states above and below the median, respectively. To obtain these quantiles, a state-month panel of log synthetic benefits per population is regressed on the set of control variables as well as

> 64To implement the local-to-zero approach, we dropped a few control variables to ensure the covariance matrix would remain non-singular. The point estimates are robust to such changes in the set of controls.

55
**6.78*****|**-0.81*****|**7.59*****|**6.73*****|**-0.86*****|
||(0.11)|(0.12)|(0.17)|(0.12)|(0.13)|(0.18)|
|No. Observations|3955|4085|8040|3955|4085|8040|
|||**Estimate**<br>|**s of Mobilit**|**y fromQuadra**|**tic CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|7.59|6.78||**_IGRC_**|0.27|0.33|
|**_ES5 _**|9.21|8.76|||||
|**_ES10_**|10.02|9.75|||||
|**_ES16_**|11.91|12.06|||||

Notes: (1) The data used are CFPS 2016 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1
# **Appendix B: Data**

The data used in this analysis are taken from the BNLA, which is designed to trace the settlement journey of humanitarian migrants from their early months in Australia to their eligibility for citizenship. This is the largest survey of humanitarian migrants in Australia, and one of the largest studies of its type in the world (Edwards et al., 2018).<sup>16</sup> The first wave of data collection was undertaken between October 2013 and March 2014, and additional waves have been conducted annually using face-to-face interviews (in waves 1, 3 and 5) and telephone interviews (in waves 2 and 4). To be eligible for the study, participants had to have arrived in Australia in the three to six months preceding the start of wave 1 fieldwork and already be holding a permanent protection visa (the ‘offshore’ group), or to have been granted a permanent protection visa in the previous three to six months after their arrival in Australia by boat or on another visa type (the ‘onshore’ group). A total of 1,509 Principal Applicants, 755 adult Secondary Applicants and 135 adolescent Secondary Applicants (aged 15 to 17 years) were recruited to wave 1, yielding 2,399 participants in total. Figure B1 shows that refugees in the BNLA survey came from 16 countries with the highest share from Iraq (40%), followed by Afghanistan (26%), and the Islamic Republic of Iran (12%) _._ The attrition rate in BNLA is relatively lower than that in other surveys in Australia (Flatau et al., 2015). Among wave 1, 2,009 respondents (84%) were re-interviewed in wave 2, 1,894 (79%) in wave 3; 1,929 (80%) in wave 4, and 1,881 (78%) in wave 5.

A unique feature of this study is that it covers a wide range of topics including
based on the war-routing strategy of the Taiping Army. The number of neighboring provinces is controlled for because prefectures near multiple provincial borders were less tightly controlled by the provincial government; they were also more prone to rebellions, due to free-riding and monitoring difficulties stemming from a greater distance to the provincial government. All geographical variables are from China Historical GIS Data (Bol 2002). Since the treaty ports system has had strong impacts on population growth and income (Jia 2014), we control for the duration (in years) of being treaty ports before 1949 (Yan et al. 1955), and the duration of being concessions and leased territories (Fei 1991).

> 35 The year 1860 was the year after which the vast majority of Jiangsu and Zhejiang provinces were occupied by the Taiping Army. This definition largely coincides with our definition except for Jiangning and Zhenjiang prefectures (under the Qing definition), which were occupied before 1860. When using the alternative definition of the Late Taiping period based on whether a prefecture was occupied largely before or after 1860, if a prefecture was occupied longer after 1860 than before 1860, we define this prefecture as a Late Taiping area. For example, if prefecture _A_ is occupied by the Taiping Army for 11 months before 1860, and for 20 months after 1860, then we define _A_ as a Late Taiping area. In this case, Jiangning and Zhengjiang prefectures (of Jiangsu) are included in the Early Taiping areas. The qualitative results (available upon request) remain the same.

24
lyzed using United States data (Antr`as, Fort and Tintelnot, 2017; Goldberg and Reed, 2023). To the extent that import monopolization raises costs of input procurement in global value chains, it may impede countries’ efforts to diversify exports away from natural resources.

We provide evidence suggesting trade policy is a channel for import monopolization in natural resource-dependent economies. Figure 1b shows the commodity export share is positively associated with higher import tariffs. We also find the commodity export share is associated with greater use of non-tariff measures (NTMs) such as quotas, that limit entry into imports.

Trade protection increases import market concentration through direct and indirect mechanisms. The direct mechanism is that trade protection, ceteris paribus, both reduces demand and raises the cost of importing, which could lead firms to exit and increase concentration. Our evidence is consistent with this mechanism: imported product markets with higher tariffs are more concentrated, conditional on country-year and product-year fixed effects. The country-year fixed effects rule out this evidence being driven by differences in national market size or institutions, and the product-year fixed effects rule out this evidence being driven by differences in the global product market, like technology or the product’s share in the import basket of a typical developing country.<sup>3</sup> The indirect mechanism is tariff avoidance. Certain importers may be able to avoid paying tariffs, either by securing legal exemptions or by illegal tariff evasion. They thus achieve an asymmetric cost advantage that allows them to sell imports more cheaply than firms that cannot avoid tariffs, expanding their market share and increasing market concentration. The seminal literature on rent-seeking suggested as much, arguing tariff avoidance could explain the persistence of high tariffs, as elites that enjoy the advantage of avoidance are a constituency in favor of tariffs remaining high (Tullock, 1967; Krueger, 1974).

> 3Our data, which begin in 1997, succeed major tariff reforms in most countries, so we are unable to exploit within country-product variation in trade policy that would allow
Table 1: Sample selection and observation numbers This table summarizes the different numbers of observations used in the different analyses based on the sample selection criteria. In each case, for a particular combination ( _i, t_ ) the CPIA, FD (or all its constituents), and the controls need to be available. The sample selection relates to the number of countries _i_ and the number of observations <u>(</u> _i, t_ <u>).</u>

|required|linear|clustering|clustering|clustering|clustering|
|---|---|---|---|---|---|
|full|panel|(2-dim)|with|(7-dim)|with|
|1998–2019|model||controls||controls|
|CPIA||yes||yes||
|FD||yes||||
|FD consituents||||yes||
|controls||||||
|nr. countries|137|125|118|49|54|
|nr. observations|2896|2750|2236|1078|1068|

for our clustering analyses without controls, we only require the CPIA and financial development proxies to exist. Given that clustering is performed with time-varying cluster means, we require the variables to be present for all times. For the panel data analysis, we take all observations into account for which we observe the CPIA, financial development proxies, and the other controls.

Averages of CPIA and FD are presented in Figure 1. The time-series averages per country in Figure 1a show some expected differences, such as higher financial development scores in economies such as for instance the Republic of Korea, Brazil, China, India, and South Africa, and lower scores in most of Sub-Saharan Africa.

The CPIA scores are only partially correlated with the FD scores, at best. Some countries that have relatively low FD scores can at the
2015 escaped poverty in 2019. About 3 percent of smallholders transitioned from being poor in 2012 and escaped poverty in 2015 and 2019. Nearly one-tenth of smallholder farmers were poor in 2012 and 2019 but not in 2015, and about 4 percent were poor in 2015 but not in 2012 and 2019. These transitions in and out of poverty define chronic and transitory poverty in Figure 3. A household that was poor in all three survey waves is chronically poor, while one that was not poor in all waves was never poor. A household is transient poor if it was poor in one or two survey waves.

Figure 2: Poverty transitions among rural households over three surveys: 2012, 2015, 2019 <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20% 44.6 40.5 55.7 43.8 53.1 39.4 45.2 49.1 41.7 46.1<br>26.6<br>10%<br>0%<br>PPP NNN PPN PNN PNP NPP NPN NNP<br><!-- End of picture text --> Notes: Own calculations from RALS (2012-2019). PPP and NNN signal poor and not poor in all three waves, respectively, whereas alternating P and N letters signals whether a household was poor (P) or not poor (N) in a given survey year. For example, PPN means poor in 2012 and 2015 but not in 2019. Figure 3: Proportion of chronic, transient, and never poor rural households between 2012 and 2019 in Zambia <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>
example, inventors from Australia, Canada, the United Kingdom, and the United States.<sup>7</sup> Recently, the World Intellectual Property Organization (WIPO) released a new dataset on inventors of PCT applications containing not only their current country of residence but also their nationality, representing a promising data source for migration research (Miguelez and Fink 2013). The PCT is an international treaty administered by WIPO offering an advantageous route for seeking for patent protection in more than one jurisdiction (in its 148 contracting states). In general, patent rights only apply in the jurisdiction granting them, be it national (e.g., USPTO) or regional (e.g., the Africa Regional Intellectual Property Organization). To seek for patent protection in multiple countries, applicants need to apply for patents in multiple offices. One simplifying route for doing this is offered by the PCT treaty. In short, by choosing the PCT route, applicants gain additional time—typically 18 months—to decide whether to pursue patent protection internationally and, if so, in which jurisdictions. An international patent right, as such, does not exist, so the applicants still have to apply for patent protection in all countries in which they eventually seek protection. However, the additional time gained by choosing the PCT route is valuable for applicants, as witnessed by the increasing share of international patent protection going through this system (about 54% of multi-jurisdictional applications in 2010).

For the purpose of migration analysis, inventor information retrieved from PCT applications presents several advantages, such as being associated with the most valuable inventions (van Zeebroeck and van Pottelsberghe de la Potterie 2011) or the

> 7. See appendix S3 for a discussion of advantages and disadvantages of name recognition systems to identify the migratory background of inventors, alongside a number of robustness checks carried out using this alternative approach.

12
<!-- Start of picture text --> South Africa<br>Uganda<br>Tanzania<br>Kenya<br>Senegal<br>Cameroon<br>Madagascar<br>Cape Verde<br>Niger<br>Burkina Faso<br>Benin<br>01234567<br>%of GDP<br>Source: Eberhard and others 2008, using World Bank 2007 data.<br>Note: Economic cost is estimated as the value of lost load<br>multiplied by the volume of load shedding. Value of lost load is<br>derived from country-specific estimates based on enterprise<br>survey data for sales lost due to power outages.<br><!-- End of picture text -->
percent of clients have fewer than 5 employees, 33 percent between 5 and 20 employees, 29 percent between 21 and 99 employees, and 22 percent of clients have 100 or more employees.

The second block of rows in Table 1 looks at what external signals of quality these providers use. It is very rare to use any form of money-back guarantee, with only 2 percent of providers offering a financial guarantee if customers are not satisfied – it is only a few marketing firms that offer this. Just over half say they will redo work if the customer is not satisfied, and 28 percent of the accounting firms say they will help clients if they fail an audit or have tax problems. Three-quarters of staff have tertiary qualifications, and 84 percent belong to some form of professional association, but only 39 percent of their staff on average have a professional certification of some form. Note that our sampling process captures the more formal and experienced providers who serve more customers – there are also likely to be some single-person firms providing accounting or marketing to a much more limited client base, and where information and quality concerns may be even more of an issue.

We then asked the providers how they find the majority of clients. The most common method is through word-of-mouth, which is the main method for 66 percent of firms. Only 16 percent of firms get most customers through advertising, and only 8 percent are walk-in customers. This reliance on word of mouth and lack of advertising is consistent with the lack of brand recognition seen in our survey of GEM firms, and shows firms using informal reputation as the main way of trying to overcome information and quality concerns.

Finally, we asked the providers why they thought more small firms do not use the types of services that their firm provides. The most common reason given was that firms lack knowledge of the value of these services, which 75 percent of providers gave as one of the three most important factors. Only 25 percent thought the services do not have a high enough return to
escalated. As of December 2019, the number of internally displaced people is estimated to be more than 2.7 million (IDMC 2020), around 1.3% of the total population (World Bank 2020b), while an additional 300,000 Nigerian refugees are hosted in surrounding countries (UNHCR 2020). The World Bank (2020b) estimates that 39.1% of the population was living on less than $1.90 a day in 2018, while 46.4% were estimated to be poor by the global MPI in the same year (UNDP & OPHI 2020). In Nigeria, 18% of the population resided in households headed by women as of 2018.<sup>9</sup> The UN Women database on gender-based violence shows that over 17% of women and girls are subject to lifetime physical and/or sexual intimate partner violence, over 43% of girls are subject to child marriage, and the prevalence of female genital mutilation is over 18%.<sup>10</sup> The total number of IDPs in **Somalia** as of December 2019 was more than 2.7 million – roughly 17.5% of the total population (World Bank 2020a), almost all of whom were displaced by natural disasters, conflict and violence (IDMC 2020). In 2020, UNHCR reported that floods and droughts explained about 80% of the internal displacement, while conflict explained 18%.<sup>11</sup> The country has seen nearly three decades of recurrent conflict since the collapse of the government in 1991. Recent years have seen the rise of the terrorist militia al-Shabaab, leading to large-scale displacement in the country. Somalia is heavily dependent on international assistance and humanitarian aid, and economic opportunities for the population are scarce. As a result, and due to the impacts of climate change such as increased flooding and severe droughts, the last decade has observed worsening acute food insecurity and famines that have forced many people
**|0.0605**|0.9283***|
||(0.0054)|(0.0069)|(0.0037)|(0.0046)|(0.0264)|(0.0580)|
|Ethnic minorities (yes=1, Kinh=0)|0.0480***|0.2035***|-0.0761***|-0.0794***|-0.1935***|-0.5107***|
||(0.0106)|(0.0243)|(0.0106)|(0.0149)|(0.0433)|(0.1390)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1671***|0.0978***|0.0496***|0.0196|6.5815***|-0.3888*|
||(0.0330)|(0.0362)|(0.0118)|(0.0194)|(0.1378)|(0.2099)|
|Observations|41,780|41,780|41,780|41,780|7,773|41,780|
|R-squared|0.266|0.154|0.048|0.050|0.073|0.078|

Note: This table reports district fixed-effect RDD regressions of employment of individuals using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1 53
- 21 -

significantly, as shown in Table 1. Though, the appliance ownership is still an important determinant of the residential demand for electricity. Thus, a number of dummy variables for various household appliances are included in _X_ 1 as independent variables.

**Alternative energy choice.** Fourth, if there are any important alternative energy sources, such as liquefied petroleum gas (LPG) and firewood, those substitutes should be incorporated in the estimation model. Ideally, with sufficient data the model should be estimated as a system of multiple demand functions (e.g., Nauges and van den Berg, 2009). However, data are not always available. Even in a single equation model, the demand function can also involve prices of alternative sources (Filippini and Pachauri, 2004). An alternative approach may be to introduce another equation for deciding whether or not to connect the service (Basani _et al_ ., 2008). Behind this approach, it is implicitly assumed that some alternatives exist. Given this specification, the conventional Hausman self-selection model can be used for estimation.

In the case of Albania, there are few important alternative energy choices for households. The country has already achieved almost universal access to electricity network, though the quality of supply still remains problematic. The access rate was 99.97 percent in 2005. People may use LPG for heating and especially for cooking, but the use of gas may be relatively limited, in particular in urban areas.<sup>14</sup> By contrast, many firms are equipped with their own private backup facilities.<sup>15</sup> Therefore, no alternative choice is explicitly considered in our empirical model. The paper takes into account whether households are using electricity or LPG for major household activities, such as heating and cooking. This is expected to

> 14 There is some difference between the LSMS data and people’s perception about the energy alternative. While the LSMS data indicate that some amount of household expenditure
without any provision on government procurement. The _TAPROC_ dummy captures instead country pairs that are involved in TAs that explicitly include a chapter on government procurement. In extensions, we also estimate the impact of single provisions on government procurement as collected in the Deep Trade Agreements (DTA) database (Mattoo et al., 2020). The variable _WTOGPA_ equals one for country pairs where both countries are members of the WTO GPA.<sup>17</sup> Among the 62 countries of our sample, 28 are EU members at some point in time. We isolate the distinctive role of the EU by adding a dummy for – EU membership. The TA variables are mutually exclusive i.e., the _TANOPROC_ and _TAPROC_ dummies are turned to zero for EU countries when the _EU_ indicator is equal to one.

We first compare estimates of the trade cost function across public and private markets, for goods and services. We focus on the estimates of the coefficient on the _SMCTY_ dummy as it captures the border effect and hence can used as a first indicator of bias towards local purchases. We then assess whether policy efforts to liberalize government procurement markets have increased cross-border flows. Specifically, we test if the coefficients on the TA and GPA dummies are positive and significant and if they are higher for government than for private flows. The estimated coefficients on these trade policy variables in eq (7) are likely to be biased because the specification does not fully control for unobserved time-invariant heterogeneity that can drive both the propensity to increase cooperation through various agreements and trade flows (Baier and Bergstrand, 2007). To control for this unobserved heterogeneity, we estimate the following specification: where the _γ_ ’s terms are bilateral fixed effects that capture unobserved and time-invariant determinants of trade costs.<sup>18</sup> Our interest is in the (partial) effect of trade agreements.<sup>19</sup> We isolate the effect of

> 17While the agreement entered officially into force in 1996, it was