rafmacalaba/data-use-mentions-extended huggingface dataset:
skilled at observing birds or are more likely to report having seen a greater number of birds, then the post-transference increase in bird abundance could be due to changes in birder composition rather than representing an increase in the population of birds.

Our primary dataset of bird abundance within treatment and control protected areas includes observations submitted by 8,255 unique birders. We download all eBird data for Africa between 1998 and 2022. We filter the data to observations submitted by our 8,255 birders outside our treatment and control protected areas. That is, we define birder skill using different data than that in our primary specification. We shape these data with the same steps we used to create our primary dataset (dropping the top 1% of bird abundance observations, imputing missing effort variables with mean values, and creating indicator variables for initially missing values). We regress log bird abundance on trips outside our treatment and control protected areas on trip duration, number of observers, hour of day fixed effects, indicators for whether each of trip duration, number of observers, and hour of day are missing, and calendar year fixed effects. We save the residuals and calculate the average value of the residuals for each birder. A birder’s average residual value represents their skill (or alternatively, the excess number of birds they typically report) because the residuals are the log number of birds reported net of birder effort and hour of day and calendar year constants. Finally, we calculate the median residual value across all birders.

We then create a new variable in our primary dataset: an indicator that equals 1 if the observation is from an above median skill birder and equals 0 otherwise. We assume the 700 birders with zero observations outside our treatment and control protected areas are below median skill. We repeat our dynamic difference-in-differences estimator with this indicator as the dependent variable. Unlike in our primary specification, the only control variables are calendar year and protected area fixed effects. Rather than estimating an increase in birder skill, which would indicate that
br>Share of|
|40<br>45<br>50<br><br>1987<br>1989<br>1991<br>1993<br>1995<br>1997<br>1999<br>2001<br>2003<br>2005<br>2007<br>2009<br>2011<br>2013<br>2015<br>2017<br>Employment as<br>po<br>0<br>1946<br>1949<br>1952<br>1955<br>1958<br>1961<br>1964<br>1967<br>1970<br>1973<br>1976<br>1979<br>1982<br>1985<br>1988<br>1991<br>Year of birth<br>No certificate<br>Primary<br>Secondary|
|Male<br>Female<br>Total<br>Post-secondary<br>Tertiary|
|Source: Authors’ calculations based on data from DOSM<br>Note: Employment data for 1991 and 1994 are not available.<br>Source: Authors’ calculations based on data from DOSM<br>**Table 6: Employment and share of employment by sector, 1987-2018**<br>**Employment by sector ('000)**<br>**Share of employment (%)**|
|**1987**<br>**1997**<br>**2007**<br>**2018**<br>**1987**<br>**1997**<br>**2007**<br>**2018**|
|Agriculture<br>1,846.40<br>1,481.30<br>1,558.20<br>1,570.30<br>
_Services PTAs_ countries and, importantly, hardly any sectoral breakdown (often only Transport, Travel and Other Commercial Services). As such, our empirical analyses are based upon trade data spanning the most recent years, i.e. nearly two decades that saw most of the action in the rise of services PTAs.

For the most detailed services sector-level analysis, we retain trade data for three sectors from ITPD-E: (i) Finance and Insurance services, (ii) Other Business services, and (iii) charges for intellectual property rights (IPR), respectively. These services meet two criteria: they are quantitatively important and well covered in balance of payments trade statistics, and they are regulation-intensive and therefore international trade in these services likely to respond to ambitious provisions in deep PTAs (unlike Travel or Transport services).

# **3.3 Sourcing of Value-Added**

The second set of research questions that we address relates services PTAs to the share of services value-added (VA) in a country’s exports that originates in PTA partners.

To construct our measures of value-added, we resort to the OECD Trade in ValueAdded (TiVA) 2018 dataset. Through use of inter-country input-output tables, TiVA provides a series of indicators on 64 countries and 36 industries, over the 2005-2015 period.<sup>10</sup> We exploit the “Origin of value added in gross exports” (EXGR ~~B~~ SCI) indicator, which offers a breakdown of country’s _j_ gross exports in industry _h_ by the value-added generated by industry _p_ in country _i_ . This allows us to identify two main dimensions of interest:

1. Services value-added from country _i_ in services exports of country _j_ .

> 10The sample of countries and the time period available in the TiVA data implies a restriction of our sample of agreements in this part of our analysis, from 143 to 106.

18
The basin’s population, which amounts to nearly fifteen million, has increased by 53.4% over the past 30 years and represents 42.4% of East Java’s population (2000) with a density of 1,249 (Table 3). In 2001, the population of municipalities accounted for 25.3% of the total population in the Brantas River basin, while that for East Java Province was 10.7%. This is due to the presence of the two important industrial cities of Surabaya and Malang in the basin, which have grown to 2.5 million and 743,0000020inhabitants, respectively. With these major cities, the GDP for the basin has increased 225 fold in Rupiah terms over the past 30 years and in 2000 contributed 56% to East Java’s GDP. In that year, industrial production in the basin – a significant 77% of East Java’s entire industrial production – was of the order of RP 7.724 trillion (~ US$ 858 million). The trend of GDP in East Java and the Brantas Basin is shown in Figure 2.

It should be noted that though industrial employment has grown from a mere 30,000 in 1970 to 959,300 in 2000, the bulk of basin residents still work in the agricultural sector with small- to medium-sized land holdings. Irrigators comprised 76% of surface water diversions in the Brantas in 2000, while industrial and domestic uses diverted 9% and 14%, respectively. (PJT I 2000 data).

**Table 3: Population**

|**Location**|**Area**<br>**km**<sup>**2**</sup>|**Population**<br>**2000**|**Density**<br>**(Per km**<sup>**2**</sup>**)**<br>
On the other hand, as the bottom panel of Figure 6 shows, there is no evidence that separations have increased because of the pandemic and lockdown. In fact, the actual number of separations is below what would have been expected if the 2018-19 trends had continued in 2020. This is true for layoffs, quits, and contract terminations (Appendix Figure A.1). Certainly, in the case of layoffs, this can be explained by the government measures to protect jobs by prohibiting layoffs in affected industries and by tying income support to the maintenance of employment relationships. The reduction in quits is not surprising since one would expect fewer workers to leave their jobs in a deteriorating labor market. It should be noticed that there is initial evidence of a small uptick in the number of separations at the end of the period, which may reflect the easing of the layoff restrictions.

Using monthly data on net labor market flows, we observe that the crisis has affected sectors and occupations differently. Table 2 presents the difference between 2020 and 2019 in the size of net flows (new hires minus separations) for January, February, March, and April by sector of economic activity. The results suggest that the accommodation and food sector was particularly affected by the crisis. The negative impacts were especially severe in March and April, the months in which tourism would normally be gearing up for the summer season. In March, accommodation and food services accounted for 52% of the 2020 net job decreases, relative to 2019, while by April, this share was 84%.

**<u>Table 2. Comparison of net job flows between 2019 and 2020 by sector</u>**

||Diff<br>|erence: Month<br>|2020–Mont<br>|h 2019<br>|
|---|---|---|---|---|
|NACE Rev. 2 sector of economic activity:|January|
0.277]|[0.606]|[0.290]||
|CDPE index|0.000|0.161|0.153|0.168|501|
||(1.000)|(0.086)<sup>_∗_</sup>|(0.104)|(0.101)<sup>_∗_</sup>||
|||[0.145]|[0.203]|[0.208]||
|Contributions to clinic index|0.000|0.024|0.044|0.002|501|
||(1.000)|(0.097)|(0.113)|(0.111)||
|||[0.678]|[0.606]|[0.969]||
|Water and sanitation index|0.000|0.122|0.069|0.177|5003|
||(1.000)|(0.062)<sup>_∗∗_</sup>|(0.071)|(0.071)<sup>_∗∗_</sup>||
|||[0.145]|[0.330]|[0.072]<sup>_∗_</sup>||
|Economic outcomes index|0.000|0.057|0.063|0.051|5003|
||(1.000)|(0.051)|(0.060)|(0.061)||
|||[0.277]|[0.330]|[0.473]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet
Detailed estimation results can be found in the supplemental appendix. Exchange rate as of November 2011: 1 USD = 607 FRW.

_Source:_ Household data set 2011/2012.

Looking at _total energy expenditure_ (Table 6. Expenditures per Month per Category (in FRW)

||Treatment|Control|_ITT_|_p_‐<br>value|
|---|---|---|---|---|
|Candles|42|109|_‐20_|<br>.339|
|Kerosene for lighting|155|609|_‐418_|<br>.000|
|Big batteries (Type D)|358|352|_‐9_|<br>.750|
|Small batteries (Type AA)|30|72|_‐43_|<br>.003|
|Mobile phone charging|407|520|_‐68_|<br>.407|
|Total traditional energy sources (without cooking energy)|993|1,662|_‐557_|<br>.000|
|Total expenditures|37,971|31,334|_7,249_|<br>.276|
|Share of energy expenditure on total expenditures|0.04|0.07|_‐0.03_|<br>.001|

_Note:_ The ITT depicts the difference in means at the follow‐up stage between the whole treatment and control group, including also non‐complying households. We control for all stratification and re‐randomization characteristics. Detailed estimation results can be found in the supplemental appendix. Exchange rate as of November 2011: 1 USD = 607 FRW).

_Source:_ Household data set 2011/2012.

As a consequence, we observe a significant reduction in expenditures for small batteries, but not for the larger batteries since households use their Pico‐PV kit predominantly for lighting but only very seldom to run their radio. The
, 1984; Robertson and<br>Hughs, 1978; Spencer and Byerlee, 1976; and Weinrich, 1975).<br>Combining these estimates with the labor force data in Table 1, we<br>estimate that about 20 percent of Africa's rural labor force is<br>channeled through labor markets (60 percent of nonfarm workersxa<br>15 percent employment share+15 percent of agricultural laborx<br>an 85 percent rural employment share-<br>22 percent of rural<br>employment). Further evidence indicates that exchange labor<br>rarely exceeds five percent of labor flows (Hill, 1977; ILO 1985b<br>and 1985c; Norman, Pryor and Gibbs, 1979; Oates, 1984; Weinrich,<br>1975).|
the matching algorithm, but it would not necessarily render the methodology inapplicable.<sup>36</sup> In any event, our analysis suggests that this approach could prove useful for both regulatory authorities and researchers.

# **VI: Conclusions**

The results of this paper challenge the dominant view that Bitcoin is little used for transactions purposes (other than buying other cryptocurrencies), and that its value is almost entirely based on speculation. Exploiting extraordinarily rich and detailed public data on trades in 135 fiat currencies, we have presented what we believe to be the most systematic and detailed evidence to date on how crypto is used as a vehicle for international capital transfers, as well as for domestic transactions. Our main identification technique takes advantage of the fact that trades are reported to eight decimal places, and the data set gives a timestamp, price, and currency for each trade. To bolster our analysis, we have also looked an interesting natural experiment of a three-day power outage in the Venezuela, where crypto has been a popular vehicle for private citizens to evade draconian capital controls, and perhaps also for official entities to avoid US sanctions. We find that the power cut had a massive footprint not only in the Venezuelan Bitcoin market, but also in the Bitcoin markets of its major destination and remittance-sending countries. Indeed, although we do not know whether the Venezuelan experience can be extrapolated to other markets, it suggests that our estimate of share of trades that are crypto vehicle trades is very much a lower bound. As such, our work complements and considerably extends previous studies that indirectly attempt to identify transactions use based on network analysis of on-chain transactions, which do not include payment currency and are only a small share of the Bitcoin trade universe.

Even though crypto exchanges are subject to regulation in the country where they are based, offchain transactions, particularly smaller ones, are difficult to systematically audit, especially where the regulator in one country needs information on a centralized exchange in another, Finland in the case of
highest risk factor, after malnutrition, which drives the most deaths in Nepal. The IHME GDB study also estimated the number of deaths and cost of health damages from PM2.5 exposure in Nepal (Table 6). The number of deaths from PM2.5 exposure in Nepal is highest in South Asia. Further, the average annual ambient (outdoor) concentrations of PM2.5 reached 83 μg/m<sup>3</sup> in 2019, which is way above the WHO guidelines of 10 μg/m<sup>3</sup> . Most of the deaths associated with ambient PM2.5 are from heart disease, stroke, COPD, lung cancer, and ALRI (World Bank, 2021).

Assuming one-fourth of the country’s total ambient air pollution (AAP) related PM2.5 emissions are attributed to brick production, the annual cost of health damages from PM2.5 exposure from brick kilns is estimated at US$ 1,161 million in PPP and the annual number of deaths at 4,487 in 2019 (Table 6). This makes PM2.5 exposure one of the main risk factors for the number of deaths after malnutrition and before high blood pressure, dietary risks, tobacco smoking, and diabetes, among dozens of risk factors assessed by the GBD 2019 study.

The estimated social cost of PM2.5 varies by kiln technology and by province. In general, the social cost of PM2.5 from brick production is higher for less energy-efficient traditional kilns and lower for more energy-efficient modern kilns (Table 7). For example, the average social cost of brick production from FC BTK is almost double when compared to ZZK. Further, the social

> 19 Fine inhalable PM (PM2.5) comprises a portion of PM10. A recent ambient air quality monitoring study (Islam et al., 2022) shows PM2.5 to PM10 ratios ranges from 0.50 to 0.57 in Kathmandu Valley.

24
international remittances identified in the data are a strict subset of households with international migrants, therefore constituting a smaller number of observations. Subsequently, a discussion of robustness of the base results follows. The main results are

> 8 Other proxies for community characteristics that have been controlled for in checking for robustness in the estimations are village home ownership rate, access to clean water, existence of a bank in the village, existence of a slum area in the village, and electricity coverage. 9 The full set of remittance results, though not all presented, is available from the authors upon request.

14
# **5.4 Durables vs. non-durables**

Much of the previous literature implies that the effect of an uncertainty shock should be channeled primarily through durable consumption, although there is no consensus on the extent to which the relationship at the microeconomic level translates into macroeconomics. In order to assess this nexus we would need bilateral monthly data by product category. As discussed earlier in the paper, these data are not available for a wide set of countries and for a sufficiently long time span. Hence, we use an indirect method to categorize trade by its durability. We compute the share of durables exported by exporter _o_ to importer _d_ as a share of total imports of _d_ . We identify the shares of durables by combining ISIC3 industry level trade data from Comtrade with the classification of durable and non-durable industries compiled by Kroszner et al. (2007). Then we order all the relationships of an importer by their durability, split them into quartiles and estimate separate regressions for each quartile. In our definition the top quartile refer to relationships most intensive in durables trade. For example looking at the US as an importer the relationship most intensive in durables is with Japan from where more than 80% of total imports comprise durable goods. The relationship with lowest durability is with New Zealand that only provides 28% durables in its total exports to the United States.

Tables 8 and 9 show that most of the negative effect of uncertainty on trade is generated by relationships that fall into the second and third quartile of durability. Uncertainty shocks are found to have an insignificant effect in the relationships in the top and bottom quartiles of durability. Without an explicit test of durability that uses industry level data, we cannot unambiguously determine the differential impact of uncertainty on goods of different durability. Despite this however, the lack of significance for the effect of uncertainty on the bottom quartile of durability confirms that the latter is an important dimension for explaining the relationship between uncertainty and trade. On the other hand, the results for the top quartile
al., 2009). As already mentioned, malnutrition can be an obstacle to adherence (Weiser, 2003). A negative shock on income or food access that may occur in the event of a food crisis, and more generally, of a global economic crisis, could therefore act a strong determinant of non-adherence to treatment.

We use household level panel data from Mozambique collected in 2007 and 2008, to evaluate the extent to which the effects of the food crisis differed between households with identified HIVpositive patients and households with no identified HIV-positive person. The baseline of the survey was fielded shortly before the peak of the food crisis while the follow-up survey was fielded when the food crisis was already winding down. Moreover, we purposely sampled households with and without an identified HIV-positive member. Overall, the timing of the survey and the sampling strategy make our data uniquely fitted for contrasting the effects of large economic shocks on HIV-patients’ households and other households.

The paper makes two main contributions to the literature on the interactions between economic conditions on the one hand, and access to treatment and adherence to treatment regimen on the other hand. First, we show that access to treatment help shield HIV households from the effects of the food crisis in Mozambique. Our analysis uncovered that welfare, measured in terms of income, food consumption and anthropometric outcomes, has deteriorated for both HIV and comparison households between 2007 and 2008. The welfare loss was, however, less severe for HIV households than for comparison households. We find evidence supporting the hypothesis that initiation of treatment and better services in the health facilities have counter-balanced the effect of the crisis by improving the health of patients and their labor force participation.

Second, we find that change in welfare has no significant effect on the frequency of visits to the health facility of patients and on their treatment outcomes, measured by the change in the CD4 count. We find, however, that people who experienced a negative income shock also experienced a reduction or a slower progression in their CD4 counts.
Ghosh, 2012). Some exceptions include, for example, Federico _et al._ (2014) who show – using a quarterly legal reserve requirement dataset for 52 countries dating back as early as 1970 – that macroprudential policies are much more frequently changed in developing and emerging economies (on average, once every 2 years) than in industrial countries (on average, once every 12 years). In particular, they show that in developing and emerging economies, this frequent change in macroprudential policy follows a countercyclical behavior (i.e., central banks reduce reserve requirements during episodes of capital outflows and output contractions), typically acting as a substitute of monetary policy which, unlike industrial countries, is often procyclical (i.e., central bank policy interest rates increase during episodes of capital outflows and output contractions).<sup>3</sup><sup>_,_4</sup> In other words, during bad times, for example, when capital is flowing out and credibility is at a low point, monetary policy is used in a procyclical manner in order to defend the currency and fight inflationary pressures, while macroprudential policy provides a second instrument that is used for macroeconomic stabilization purposes.<sup>5</sup><sup>_,_6</sup> When focusing on the effects of macroprudential policy, most studies typically analyze the impact on domestic credit conditions (e.g., Montoro and Moreno, 2011; Terrier _et al._ , 2011; Crowe _et al._ , 2013; Lim _et al._ , 2011; Tovar _et al._ , 2012) and economic activity (e.g. Glocker and Towbin, 2012). However, the effectiveness of macroprudential policy as a macroeconomic stabilizing tool is still very much an open question.

The purpose of this paper is to contribute to the empirical literature on the macroeconomic effects of macroprudential policy, by focusing on _legal_ reserve requirements (hereafter RR). The focus on
increase the concentration of low-skilled migrants compared to the concentration of high-skilled migrants.

Our relative concentration measure is defined as the following: A nice property of this bilateral measure is that its sum across destination countries boils down to the difference between Herfindhal indices for high-skilled and low-skilled migrants. The equation to be estimated for relative concentration can be written as the following: Similar to previous two equations, we have αo and αd as the origin and destination fixed effects, respectively, Dod is a vector of explanatory bilateral variables and ηod is the error term. A negative value for β is expected if existing networks increase the concentration of low-skilled migrants across destinations compared to the concentration of the high-skilled migrants.

# 3 Key empirical issues

Our main empirical goal is to quantify the diaspora effects on the size, skill composition and concentration of migration flows as well as evaluate the robustness of the elasticity based on econometric techniques used. We use the Docquier, Lowell and Marfouk (2009, referred to as DLM from now on) database. Based on census and register information on the size and structure of immigration in all OECD countries, DLM database provides the stock of migrants from any given country to any one of the 30 OECD countries by education level for 1990 and 2000. The dataset covers only the adult population aged 25 and over, and migration is defined on the basis of the country of birth rather than citizenship<sup>1</sup> . We should note that the DLM database does not fully capture undocumented migration for which systematic statistics by education level and country of origin are not available in most destination countries. US census is believed to count most undocumented migrants, however this is not the case in many other OECD countries. By disregarding undocumented migrants (which are disproportionately unskilled), the database probably underestimates bilateral migration stocks/flows and overestimates the average level of education of the immigrant populations in many destination OECD countries.

> 1Even though this is the standard definition of a migrant, especially in the economics literature, the dataset