rafmacalaba/data-use-mentions-extended huggingface dataset:
The “high digital” occupational cluster includes 13 occupations (Table 5). All require all levels of digital skills. All also have a high share of digital skills among total skills, ranging from 28 to 58 percent. The high-digital cluster includes, for example, software developers, a high-skilled occupation, and keyboard operators, a medium-skilled occupation. Both have a similar share of their skills that is digital (56-58 percent) but software developers mostly require advanced digital skills while keyboard operators mostly require basic digital skills. The bulk of these 13 occupations are consistent with the ISCO taxonomy of information technology occupations and most of these 13 occupations also have high scores on an index of digitalization created for occupations in the United States based on O*NET data (Muro et al. 2017).<sup>24,25</sup>

**Table 5. Digital skill and number of occupations of digital levels based on clusters**

|||**Av**|**erage share **|**of digital skills (**|**%)**||
|---|---|---|---|---|---|---|
||||||||
||||||||
|**user**<br>**number**|**ga**<br>**level**|**Total**|**Advanced**|**Intermediate**|**Basic**|**umer o**<br>**occupations**|
|0|Very Low|4.8|0.8|1.0|2.9|38|
|1|L|8.1|2.1|1.7|4.2|20|
|2|ow|11.5|1.1
Western Africa. Under current border frictions, investments in connections through Congo and into Nigeria are reduced. Prioritizing the central African infrastructure gap thus requires inequality-averse planners, unrealistic trans-African trade elasticities, and/or efforts to reduce border frictions.

Methodologically, in this paper, I develop a rigorously quantitative approach to characterize economically optimal investments into large road networks. I demonstrate the use of geospatial big data and routing engines to algorithmically create large and accurate network graphs, including potential new roads, and simulate investments on them via established quantitative frameworks. I am also the first to account for cross-border frictions and trade through ports to study their implications for optimal spatial allocations. Detailed estimation of road construction/upgrading costs facilitates interpretation and enables link-level cost-benefit analysis. A corollary of the work is an easily accessible Julia implementation<sup>2</sup> of Fajgelbaum & Schaal (2020)’s MATLAB toolbox.<sup>3</sup>

> 2https://github.com/OptimalTransportNetworks/OptimalTransportNetworks.jl

> 3https://github.com/OptimalTransportNetworks/OptimalTransportNetworkToolbox

3
Some further words of explanation are in order. The information on household consumption spending comes from the socio-economic surveys that are undertaken on a continuing basis by the National Statistics Office. About 3,800 different households are surveyed every month using stratified cluster sampling, for which the weights are known: the strata are the country’s provinces, and clusters of households are surveyed in randomly-chosen villages or urban wards. When aggregated to annual or even quarterly levels, the data are nationally representative, but are somewhat less so at the monthly level.

The socio-economic surveys do not collect price data, so in order to calculate real spending we had to match price data from the Bureau of Trade and Economic Indices (2012) with the survey data. More precisely, we matched the monthly price series for about 120 items of consumer spending, which are available for each of the five major regions of Thailand, with spending categories from the socio-economic surveys, region by region. This allowed us to construct real spending for each household. We did not deflate the value of goods produced by the household for its own consumption: a higher price for such goods would work on both the income and expenditure sides of the ledger, with no net effect on wellbeing (assuming no behavioral response to price changes).

Over the four years of interest to us (2007-2010), prices in Thailand were highly variable, rising sharply in early 2008, then falling just as quickly, before resuming a steady rise in early 2009. This pattern shows up clearly in the top panel of Figure 5, which tracks the official consumer price index.

The bottom panel of Figure 5 shows price indexes that we constructed, normalized to 1 in 2007, for households in the top and bottom deciles of expenditure per capita (over the period 20072010). Starting in early 2008 there is a clear divergence, with prices rising more rapidly for poor than for affluent households. This mainly reflects the relative rise in the price of food in early 2008, which carries more
Figure 2: External Links vs. Ownership Integration <!-- Start of picture text --> 3<br>7<br>2.5<br>5<br>2<br>1.5 3<br>1<br>1<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Customer & suppliers Ownership links Customer & suppliers Ownership links<br>Ownership links to agribusinesses Ownership links to agribusinesses<br>2.5 2.5<br>2 2<br>1.5 1.5<br>1 1<br>2014 2015 2016 2017 2018 2019 2020 2021 2022 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Customer & suppliers Ownership links Customer & suppliers Ownership links<br>Ownership links to agribusinesses Ownership links to agribusinesses<br>US Fortune 500 firms<br>Av. change rel. to firm's 2014 baseline Av. change rel. to firm's 2014 baselineNon-US & US non-Fortune 500 firms<br>US Fortune 500 firms<br>Non-US & US non-Fortune 500 firm<br>Median chg. rel. to firm's 2014 baseline Median chg. rel. to firm's 2014 baseline<br><!-- End of picture text -->

**Note:** This graph compares the evolution of a firm’s number of customers and suppliers to the number of firms that it is linked to via ownership ties (either parent, subsidiary, or common parent). For each firm, the change in the number of externally and internally connected firms is computed relative to its 2014 value, before taking the average over U.S. Fortune 500 agribusiness companies (left graph) and non-U.S. based or non-Fortune 500 U.S. agribusiness companies (right graph). The upper panel shows the yearly average, and the lower panel shows the median
April 2018 and April 2019 (i.e., before and after the pilot was implemented), in our placebo experiments we compare them between April 2017 and April 2018 (i.e., one year before the implementation of the ENTRE pilot). If our main experiment is just picking up pre-existing trends, these trends should be also captured by our placebo experiments. Tables 5 and 6 show that the coefficients of the placebo regressions are all quite small in magnitude and statistically insignificant, providing strong evidence against pre-existing trends. This is further illustrated in figures 3 and 4.

Table 5. Placebo test of the impact of the program on the probability of student dropout

||<br>ITT<br>|
|---|---|
||Experiment<br>Placebo|
|**_Panel (i): Pooled Model_**||
|_ENTRE j_|-0.000<br>-0.013***|
||(0.000)<br>(0.004)|
|Observations|147,977<br>145,628|
|R-squared|0.351<br>0.331|
|**_Panel (ii): Treatment arms_**||
|_ENTRE 1 j_|-0.000<br>-0.015***|
||(0.000)<br>(0.005)|
|_ENTRE 2 j_|-0.000<br>-0.011**|
||(0.000)<br>(0.005)|
|_ENTRE 3 j_|0.000<br>-0.013**|
||(0.000)<br>(0.005)|
|Observations|147,977<br>145,628|
|R-squared|0.351<br>0.331|
|Controls at student level|YES<br
.0817|0.0819|
|Controls:|Host, Parent|Host, Parent|Host, Parent|Host, Parent|
||Bilateral|Bilateral|Bilateral|Bilateral|
|FEs:|Year, Parent|Year, Parent|Year, Parent|Year, Parent|

_Notes:_ All specifications are estimated in PPML regression using an unbalanced panel of all available host-parent-year observations for which the parent-country KF-METR is available. All specifications include parent country and year fixed effects, and the following characteristics of the host and parent countries: GDP (in log), capital account openness index, trade openness, inflation rate, government expenditure share, and exchange rates. Standard errors, in parentheses, are clustered by host. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

52
under poverty were sourced from the U.S. Census Bureau‟s Small Area Income and Poverty Estimates (SAIPE).<sup>16</sup> According to the U.S. Census Bureau‟s website, the estimates were computed using data from administrative records, intercensal population estimates, and the decennial census with direct estimates from the American Community Survey.

> 15 This principle could also be applied to other approaches to efficiency measurement, of course. One could, for example, estimate separate stochastic frontiers for different groups. 16 The data are available at http://www.census.gov/did/www/saipe/data/schools/index.html
6

**Figure 2** : **Number of transactions and proceeds**

<!-- Start of picture text --> 1,200 70<br>1,000 60<br>50<br>800<br>40<br>600<br>30<br>400<br>20<br>200 10<br>0 -<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Number of transactions Total proceeds<br>USD Billions<br><!-- End of picture text --> Source: World Bank Privatization Database 11. While 120 countries have engaged in privatization over the past 14 years, proceeds are highly concentrated in a handful of countries: over two-thirds of total developing country proceeds over the entire time period were generated in just ten countries—or 8 percent of all privatizing countries—with over half of all proceeds generated by the top five alone. While ten countries consistently generated the bulk of all proceeds, the composition of the group changed over time (Figure 3). Brazil, Argentina and Mexico dominated the 1990s, with these three countries alone accounting for virtually 50 percent of all proceeds. Argentina and Mexico fell off the list in more recent years due to near completion of much of the privatization agenda, but Brazil remained and together with China, Poland, and the Czech Republic accounted for nearly 60 percent of all proceeds since 2000. For the first time, two countries in the Middle-East and North Africa region made it to the group of ten on account of the partial sale of Saudi Telecom and the sale of Regie de Tabac (tobacco manufacturing) in Morocco. Five countries remained on the list in both periods - Brazil, China, India, Poland, and Russia – representing 41.3 percent of total proceeds from 1990-2003.
manufacturing sector continues to be dominated by low value-added industries, in particular in Central America (such as the maquillas in Mexico) (Figure 3). In fact, while the Washington consensus policies led to the demise of old and unsustainable industries, new ones did not emerge to a sufficient extent.<sup>7</sup> The absence of development of new industries also undermined export performance (Figure 4). The lack of LAC‘s structural transformation is illustrated in figures 3 and 4 which show the evolution of East Asia‘s versus LAC‘s economy. East Asia has been increasing the share of high-tech and manufacturing commodities in its exports and in its economy. Latin America to the contrary has experienced a declining share of high-tech and manufacturing exports. Without diversification and structural upgrading, Latin America is less likely to sustain growth and is more exposed to downside risks in the global economy.

<!-- Start of picture text --> Figure 3: LAC's Structure of the Economy<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Agriculture, value added Manufacturing, value added<br>Services, value added Total natural resources rents<br>% of GDP<br><!-- End of picture text --> Source: World Development Indicators.

**Figure 4: LAC and East  Asia: Structure of Exports**

<!-- Start of picture text --> 35<br>LAC 1995<br>30<br>LAC 2000<br>25<br>20 LAC 2005<br>15 LAC 2010<br>10<br>EAP 1995<br>5<br>EAP 2000<br>0<br>High-technology exports (
TABLE A18: Impact of leaders’ exposure on individual intermediary outcomes: preventive health

||**ITT Leaders**<br>(1)|**S.e**<br>(2)|**P-Value**<br>(3)|**Mean**<br>**Control**<br>(4)|**Obs**<br>(5)|
|---|---|---|---|---|---|
|**_Health_**||||||
|**Health index**|-0.00|( 0.04)|0.37|0.00|2,073|
|Insist on brushing teeth|-0.00|( 0.01)|0.78|0.02|2,073|
|Ensure a healthy diet|0.01|( 0.01)|0.05|0.06|2,073|
|Keep child away from the stove|-0.00|( 0.00)|0.88|0.00|2,073|
|<br>Ensure good hygiene|-0.01|( 0.02)|0.27|0.21|2,073|
|Wash clothes properly|-0.00|( 0.01)|0.71|0.12|2,073|
|Avoid giving coffee|0.00|( 0.00)|0.29|0.00|2,073|
|Make child wash hands|0.00|( 0.02)|0.90|0.27|2,073|
|Smoke at a distance from child|-0.00|( 0.00)|0.74|0.00
eat _healthy_ and nutritious foods, (4) _ate less_ than should have eaten, (5) had _skipped_ a meal, (6) _run out_ of food, (7) was _hungry_ but did not eat, (8) went without eating for a _whole day_ . All estimations use sampling weights to capture systematic non-response and attrition in phone surveys. Standard errors, clustered at district (woreda) level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

Finally, Table 8 presents the disaggregated DID estimates for labor market and livelihood outcomes. We use data from rounds 1-10 of the surveys for this analysis, where rounds 1-8 represent the pre-war and rounds 9-10 represent the post-war-onset periods. Again, these results confirm that pre-war trends of participation in various labor market and livelihood activities are generally statistically similar trends across households affected and unaffected by the armed conflict, as evidenced by the coefficients of the interaction between Round 2 – Round 8 dummies and the indicator for Tigray region. Furthermore, immediately after the outbreak of the war households from Tigray experienced a significant reduction in economic activities, with the war reducing participation in any economic activity by 20 - 21 percentage points.

However, the impacts vary by type of livelihood activity. Relative to the baseline, households from Tigray experienced 20 - 22 percentage point reduction in participation in nonfarm business activities in the aftermath of outbreak of the armed conflict (December 2020 and January 2021). Similarly, war-affected households reported 19 - 20 percentage point reduction in participation in wage-related activities in the first two months after the outbreak of the conflict.<sup>30</sup> The sizes of these impacts are comparable to the aggregated results in Table 5. Consistent with the aggregated results in Table 5, farm activities, however, appear to be unaffected. These findings are
realize synergies and to use Prindex as a stepping stone towards collection of household-based information to support a country-level policy dialogue that is based on local evidence rather than pre-conceived notions. Helping developing countries create the institutional preconditions for sustained and inclusive economic growth, equity, resilience and private sector development to achieve the SDGs through modernization of their land sector will require making the transition to a results-based evidence-driven approach. The approach taken in Zambia can generate the data and analysis needed to make this transition.

19
price changes under reasonable assumptions. In other words, one can use relative price data as a proxy for unobserved shocks since the two phenomena move monotonically closely together.

While Ball and Mankiw's 1995 paper offers sound justification for including skewness of the distribution of relative price changes as an explanatory variable, their earlier paper "Asymmetric Price Adjustment and Economic Fluctuations' (1994) explains the mechanism 5Positive skewness arises when there are few unusually large positive shocks and many small negative ones
welfare, however their analysis remains beyond the scope of this paper.<sup>3</sup> The paper is organized as follows. The following section reviews what is known about the impact of the crisis in the Philippines. In the course of this review, we also make some methodological comments on related literature for other countries in the region. Sections 3 and 4 respectively describe the data and our methodology. Our results are presented in Section 5. The final section sums up with some concluding observations.

# **2. What do we know about the distributional impact of the crisis?**

While it is generally believed that the Philippines escaped the worst of the regional financial crisis<sup>4</sup> , relatively little is known about the distributional impact of the crisis (which for the Philippines turned out to be a combination of financial and weather-related shocks). One strand of work for other countries in the region has involved comparisons of distributional parameters, including measures of absolute poverty, based on household survey data before and after (or during) the crisis.<sup>5</sup> For the Philippines, the latest available household survey is the 1998 Annual Poverty Indicators Survey (APIS) conducted by the National Statistics Office (NSO).<sup>6</sup> Using these data in conjunction with data from the 1997 Family Income and Expenditure Survey (FIES), Reyes, de Guzman, Manasan and Orbeta (1999) reported that per capita income declined

> 3 Some of the non-income effects may of course be mediated through changes in household incomes or consumption. An assessment of the income or consumption impact thus has some relevance for the potential magnitude of non-income effects too.

> 4 See for instance, World Bank (1999).

> 5 See, for instance, estimates in World Bank (2000). Some of this literature is also reviewed in Booth (1999). For recent estimates for Indonesia, see Suryahadi, Sudarno, Suharso,
to maintain rents at the levels that could deliver a commercial return.</mark> <mark>Moreover, free-riding could hardly be avoided. As SIP is an open area, roads built inside or connecting to SIP could also be used by users outside of SIP, including in adjacent industrial zones. At the time SIP attracted interested investors with the help of Singapore’s promotion, other industrial parks and neighborhood villages and towns watched and learned from SIP, recruited staff who received on-job training in SIP, and even lobbied to attract investors away from SIP. The competition with Suzhou New and High-tech Development Zone drew the most attention and criticism from Singaporean partners.</mark> _Source: Zhao and Farole (2011)_

> 18 As a result the 75% local retention was extended.

> 19 Data source: Suzhou Statistical Yearbook, various issues.

> 20 This was the site that was initially offered to Singapore for development of SIP

18
as of 2014. Table 6 lists the numbers of surveys conducted between 2010 and 2014 that contain some detailed information on household transport characteristics and ownership of vehicles, where many surveys are Household Income and Expenditure Surveys, Household Budget Surveys, and Demographic Health Surveys. These household surveys are not as multi-topic or integrated as LSMS surveys, but some also collect abundant information on transport.

**Table 6. Number of household surveys with transport information (2010 - 2014)**

|**Information collected**|**Number of**<br>**surveys **|**Number of**<br>**countries**|
|---|---|---|
|Reasons for not attendingschool|||
|-<br>Distance/time to school|113|56|
|-<br>Lack of access to transportation|14|10|
|Gettingto school|||
|-<br>Distance to school|25|19|
|-<br>Time to school|33|21|

> 6 http://datanavigator.ihsn.org

15
%GDP)<br><!-- End of picture text -->

# **IV. Methodology**

To evaluate the impact of being a country on the FATF list (i.e. the treatment) on tax revenues (i.e. the outcome), we compute the Average Treatment on the Treated (ATT) _ATT_ = _E_ [ _Yi_ 1 − _Yi_ 0 _NonCoop_ = 1 ] = _E_ [ _Yi_ 1 _NonCoop_ = 1 ]− _E_ [ _Yi_ 0 _NonCoop_ = 1 ] , (1) with<sup>_Y_</sup> _i_ 1<sup>the value of tax revenues for a country in the FATF list, and</sup><sup>_Y_</sup> _i_ 0<sup>the value of tax</sup> revenues in the same country had it not been on the FATF list. Unfortunately, the latter variable is not observable, and one convenient solution would be to replace it by the mean value of tax revenues in the countries that are not on the FATF list. However, the treatment—being included in the FATF list—is probably not random but dictated by some observable factors (political institutions, macroeconomic conditions, and so forth) that also determine tax revenues. Therefore, as previously pointed out, comparing the mean value of tax revenues between NonCooperative and Cooperative countries can generate a “selection on observables” problem that would lead to biased results.

To address this problem, we compute the ATT by replacing the last term of (1) by the average of tax revenues in countries that are not on the FATF list but present a set _X i_ of characteristics comparable with those of countries on the FATF list, namely _ATT_ = _E_ [ _Yi_ 1 _NonCoop_ = ,1 _X i_ ]− _E_ [ _Yi_ 0 _NonCoop_ = ,0 _X i_ ] . (2)

> **†††** The simple comparison of mean values between Cooperative and Non-Cooperative countries may be biased by
●|●||●||●||
||Vanuatu|●|●|●|●||●|●|
||Number of North Countries|22|22|21|22|21|22|22|
||Number of South Countries|178|178|57|192|95|192|148|
||Number of Offshore Centers|21|21|14|24|7|25|12|

Note: The coverage of bank loans and deposits has been extended by the use of mirror data obtained from the 47 Bank for International Settlements (BIS) reporting countries reporting assets and liabilities vis-à-vis all countries and jurisdictions (appendix, section A.1). Sources and destinations not marked with dots are not identified as separate countries by the BIS, but are included in the data reported by their governing country. For foreign direct investment (FDI), coverage was enhanced through a similar procedure, using liabilities reported by the destination country when asset data are missing (appendix, section A.3).

This table indicates, for each country in the sample, whether we have data on that country as a source and destination. A country is considered a source (destination) of international investments if, for at least one year, the sum of all international investment positions for which the country is a source (destination) of funds is non-missing. For international reserves, we have country-to-region data, so we only indicate whether a country is a source. The list of offshore financial centers (OFCs) is based on the list of countries included in the International Monetary Fund's Staff Assessments on Offshore Financial Centers (OFCs) (available at: https://www.imf.org/external/np/ofca/ofca.aspx).
are unweighted averages of 39 advanced economies and 148 EMDEs. The Trade Across Borders indicator is spliced backwards where methodological changes affected the level. An economy's score is indicated on a scale from 0 to 100, where 0 represents the lowest performance and 100 the frontier, which is constructed from the best performances across all economies and across time. “DB” before the year indicates the related Doing Business publication.

B. Trade reforms include those business reforms categorized under trade across borders in the Doing Business survey. The number of reforms is calculated using the business reforms by year and by country as listed in the World Bank’s Doing Business publications. These are codified from the text list of business reforms as reported by the Doing Business survey. C. Export concentration measured as the Herfindahl-Hirschmann Index (Product HHI). Observations for 2007 and 2017 are unweighted averages. EMDEs are based on data for 146 economies: 20 metal-exporting economies, 35 energy-exporting economies, 35 agriculture-exporting EMDEs, and 58 commodity-importing economies. Values closer to 1 indicate more concentration.

D. Based on data for 40 EMDEs.