rafmacalaba/data-use-mentions-extended huggingface dataset:
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _6_ least one other item can be found that carries very strong predictive power over its price.<sup>10</sup> Table A1 summarizes the raw data availability in each country. The table shows that the data of the analyzed countries on average include 7 food items measured across 27 markets, with around half of the survey data missing. The number of markets is generally a strong improvement in geographic detail when compared to traditional price indicators. For example, World Bank (2021) analyzes official CPI at the subnational component level in South Sudan. The analysis highlights the limits of relying purely on primary data collection, as the indexes are only compiled for the three major urban areas and not for any rural areas. This is despite an estimated 80% of the country’s population that lives in rural areas, and the fact that only a fraction of the remainder urban population buys goods at these three markets. Many urban households rely on markets that are less connected to international markets than primary markets in the capital city.

In total, the application uses data from 676 different markets. There are an additional 547 markets without observations, which are markets where WFP tracks commodities that are not covered by the application. These locations are not modeled but are spatial interpolated separately. Table A1 provides a country breakdown of data availability, for the country-product combinations analyzed by the paper. Data coverage of around 70%, as a percent of all _time × location with data_ , is relatively high, while 30% can be considered relatively low. Naturally, these figures depend on the selection of markets, food items, and the period of analysis. Higher data coverage could be achieved by focusing on a shorter time period, working with fewer markets, or tracking a very narrow basket. The statistics are thus not representative of the WFP data base, but represent the data selection of the paper which seeks to balance reasonable data coverage and data
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE 70 percent and 30 percent of the market share, respectively. At close to 60 percent, Mali’s mobile market has the highest Herfindahl-Hirschmann index in West Africa (figure 22).

**Figure 22. The mobile market in Mali is highly concentrated**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2005 2008<br>Herfindahl-Hirschmann Index<br>Mali Niger Cameroun Ghana Burkina Faso Benin Nigeria<br><!-- End of picture text --> Source: World Bank (2009a) Note: Herfindahl-Hirschmann index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly, while a lower the HHI the more diluted is the market power as exerted by one company/agent.

The second challenge is related to the Internet market. Over the last seven years, Internet penetration has increased substantially, and prices have dropped. In 2000, use of the Internet was almost nonexistent; by 2007, penetration had risen to 6 percent. The increase in access coincided with an increase in service quality. In 2000, connection speeds were very slow at less than 1 Mbps. In 2007, after Sotelma and Orange Mali had connected to SAT-3, connection speeds had reached 17 Mbps per person. Still, Mali lags behind other West African countries in both internet penetration and international internet bandwidth (figure 23).

In terms of the internet market, the institutional framework of Mali’s ICT sector, which allowed for the expansion of mobile markets, is now becoming a major deterrence for the expansion of Internet. A good illustration of the legal
would be expected to have a comparative advantage in the sectors that use these factors more intensively (Levchenko (2007); Debaere (2014)). Industries that are intensive in skilled labor and capital would also be expected to have higher capabilities, which could be transferred to Polish firms through foreign investment. Data for these measures of German influence are obtained from Eurostat, OECD and UNIDO. Finally, we use data on exports from COMTRADE to construct a measure of Germany’s revealed comparative advantage (RCA) relative to Poland and to the world.<sup>7</sup> To mitigate concerns about endogeneity, all these measures are constructed using data for Poland’s pre-accession period. For example, data on Germany’s pre-accession exports and output by sector refer to the averages over the period 1994-2004. Table A.2 in the Appendix reports summary statistics on the main measures. To conduct placebo tests, we further collected data on outputbased measures for Poland itself, Russia and other neighbour countries, notably Ukraine and Lithuania.

# **5 Empirical results**

## **5.1 Effects of German acquisitions on firm performance**

Results in this section draw on the Orbis data set, and for this reason refer only to employment as the outcome variable. Unfortunately, the variable turnover presents an excessive amount of missing values, which makes it unreliable. Figure 2 plots the regression coefficients of the event-study (equation (1)) including firm and year fixed effects. Before 2004 there is no discernible difference in employment growth between domestic and to-be-acquired firms. However, after 2004 we observe a positive effect of foreign acquisition on employment growth that persists throughout the period of analysis. Overall, average treatment effects are reported in Table A.3 and indicate that German acquisition increases firm-level employment by 15%. Positive effects are also found when using an alternative measure of foreign ownership based on the number of foreign shareholders (column 2 of Table A.3) and when excluding firms that exit
fossil fuel profiles. We address this issue by employing a simple cleaning algorithm, which provides a rule-based procedure to identify and correct inter-temporal changes in reported quantities and unit prices of pre-defined magnitudes. We provide a more extensive discussion on the cleaning algorithm in Appendix D.

> 8With its inception in 2014, the tax reform (Ley 20.780) falls too close to the end of the sample period to merit an empirical analysis. At the time of writing, the implementation of the policy remains an ongoing process; see the World Bank’s Results Brief 15 February 2021 and project Market Instruments for Climate Change Mitigation in Chile (last access: September 2022).

> 9The authors are working on a revision of the results using the newly available data.

7
Table 5: Household-level Returns to Education for Father and Children

||**Father’s G**|**eneration**|**Children’s**|**Generation**|
|---|---|---|---|---|
|Years of Education|25.977***|25.581***|18.327***|18.934***|
||(2.146)|(2.086)|(2.665)|(2.685)|
|Rural (=1)|-1.882|8.019|15.654|19.741|
||(16.644)|(16.186)|(26.260)|(26.147)|
|Years of Education_×_Rural|-14.628***|-14.272***|-5.494*|-5.787*|
||(2.446)|(2.377)|(2.942)|(2.943)|
|Number of Working Age Adults||35.157***||12.029***|
|||(3.293)||(2.586)|
|Constant|141.880***|46.038***|85.695***|49.451*|
||(14.893)|(16.752)|(24.199)|(25.510)|
|Observations|3993|3993|3396|3396|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Unit of observation is
bed occupancy. Both Figures 3 and 4 show data from 2008 and 2009 across all acute services by different types of hospitals in Croatia. There is a consistent decline in ALOS across all hospital types, but especially for clinics (0.9 days). Bed occupancy declined in the majority of hospitals, except for in clinical hospital centers. While it may be counterintuitive to think that increasing efficiency has led to decreasing bed occupancy rates, this result makes sense as hospitals now have incentives to discharge patients earlier, on average resulting in lower bed occupancy. This simple comparison of before and after cannot account for pre-existing trends, however. For example, ALOS or bed occupancy may have already been declining over time due to changes in clinical practices or technology. The rest of the paper conducts a more robust analysis to examine whether these changes can be attributed to the introduction of PPTP and/or DTS.

4
, by cyclicality of fiscal policy <!-- Start of picture text --> Percentage points<br>0.5 90% confidence interval<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>Countercyclical policy Procyclical policy<br>t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8<br><!-- End of picture text --> F. Impact on log real GDP per capita, by institutions <!-- Start of picture text --> Percentage points<br>0.1 90% confidence interval<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>High government Low government<br>effectiveness effectiveness<br>t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8 t+1 t+2 t+3 t+4 t+5 t+6 t+7 t+8<br><!-- End of picture text --> _Sources:_ EM-DAT, World Bank.

_Note:_ “t” is years.

- A. “All intensity shocks” based on 3041 observations for 156 economies over 21 years. “Large intensity shocks” based on those in the 90<sup>th</sup> percentile (or 421 observations).

- B.
components are derived from the indicators data pooled for all countries in the sample. The resulting index for each country would then be relative to the world and time sample average.

The underlying indicators for each index are listed below.'<sup>4</sup> We have selected these indicators on the basis of both their relevance in previous theoretical and empirical work and

> X2This valuation of capital is admittedly crude. We are aware that, if adjustment costs are present, conventional investment theory predicts that the market value and replacement costs of capital should diverge in the short run.

> We should note one major caveat concerning our constructed wealth measure, namely the neglect of nonreproducible assets (land, oil, etc), on whose volume and value little information is available for the vast majority of countries. Our definition of wealth in (3.4) above is admittedly incomplete in this regard, especially for resourceintensive countries.

> 14 The main data sources are the World Development Indicators (World Bank), International Financial Statistics

> (IMF), Exchange Rate Arrangements (IMF), Civil Liberties Index (Freedom House), and Kaufman et al. **(1999).**

**16**
labor productivity, TFP, will reflect demand-driven cyclical influences such as changing labor and capital utilization as well as technological and organizational changes (Basu et al., 2006; Fernald and Wang, 2016).<sup>1</sup> To account for the intensive margin of labor inputs, researchers typically scale the factor inputs using observable proxies for factor utilization. For example, average hours per worker, electricity usage, and surveys of capacity utilization can provide insight, individually and collectively, into how labor effort and capital utilization vary (Burnside et al., 1996; Imbs, 1999; Shapiro, 1993). This approach has also been extended using industrylevel data to account for industry heterogeneity. However, data requirements for this approach—in particular, annual data on the sectoral distribution of hours-worked, employment, and capital—are prohibitive for many advanced economies and most EMDEs, which makes it impossible to conduct a broad-based analysis across many economies.<sup>2</sup> Structural VARs (SVARs) take an alternative approach to removing cyclical or demand-led

> 1In the United States, one-half of TFP growth variability has been attributed to non-technology factors Basu et al. (2006).

> 2Basu et al. (2006), Huo et al. (2020), and Comin et al. (2019) have implemented these for advanced economies, but not for EMDEs. A second difficulty with this approach is the possible presence of a wide range of structural relationships between different inputs to production, preventing the broad-based application of this methodology. For example, an inflexible labor market around the number of hours worked makes it a poor proxy for utilization.

2
compared to predictions provided by the Intergovernmental Panel on Climate Change (IPCC) General Circulation Model(s) (e.g. Alcamo et al. 2003; Milly et al. 2005; Palmer et al. 2008). These methods have several shortcomings. First, global models tend to be complex and require many inputs. Second, global models are useful for global comparisons of basins; however policy work on specific treaties benefit greatly from basin specific applications. Third, the timely nature of the results does not provide continuous updates from the most current observations. Many experts see integrated monitoring systems that include remote sensing and GIS data helpful in 6
In the first panel of Table 7 we show the coefficient capturing the (pooled) impact of ENTRE on different self-reported indicators (each coefficient corresponds to a different regression). The second panel reports the number of observations (schools) corresponding to each regression. The outcome variables are expressed as changes between the baseline and follow-up surveys. We measure the impact at two different moments of follow-up: short-term (about 2 months after the training was implemented and about a month before the enrollment for the next academic year begins) and mid-term (in the middle of the next academic year, about 8 months after the training was conducted).

Table 7. Impact of the program on different self-reported indicators at school level

|_ΔY j: Change between baseline and follow-up in:_|(1)Coe|fficient|(2)Obse|rvations|
|---|---|---|---|---|
||Short-term|Medium-term|Short-term|Medium-term|
|_Dropout expectations (standardized)_|-0.22***<br>(0.08)|0.03<br>(0.06)|1,005|2,385|
|_Expected Returns to education (standardized)_|0.25**<br>(0.10)|0.02<br>(0.07)|1,005|2,255|
|_Prioritized tasks (dropout)_|0.45***<br>(0.13)|0.27***<br>(0.08)|1,005|2,385|
|_Prioritized tasks (Authorities' requests)_|-0.22**<br>(0.11)|-0.16**<br>(0.07)|1,005|2,373|
|_Beliefs about principal's influence on the dropout
_Services PTAs_

# **5 Results for Cross-Border Services Trade**

We structure the empirical analysis of services PTAs on trade in three steps, starting with establishing an aggregate impact effect to establish a baseline. The main results exploit the rich detailed WB-DTA data to unpick this overall average effect. Hence, using equation (4.1), we first estimate services PTA effects on aggregate services exports from ITPD-E. We then zoom in on specific policy configurations to shed light on those design features of PTAs that effectively matter for trade. Thirdly, we show that these effects in aggregate trade flows are driven by sizable trade effects on specific, regulation-intensive services sectors.

A general insight from our results is that aggregate average trade effects are small and often insignificant, but that highly significant impacts of policy can be found by employing more granular data, i.e. specific policy configurations or sectoral services trade flows, or both. We conclude, therefore, that the rich and novel policy information offered by the WB-DTA 2.0 is instrumental in an analysis of services trade flows, and that small or insignificant aggregate effects should not prematurely be interpreted as services PTAs being ineffective. Our results demonstrate that quite the contrary is true, and as such our paper sheds light on the design of effective service trade agreements at the provisions level.

## **5.1 Benchmark Overall PTA**

We start our analysis with a simple specification that has usually been employed in the literature, namely a binary indicator variable for the existence of a services PTA subject to a number of appropriate controls, which here consists of full sets of fixed effects and an EU membership dummy. The existence of an agreement _per se_ is associated with a very small 28
the regional indicators were not available.</mark><sup>22</sup> <mark>From the model in equation (1), we obtained the predicted probability of participation in the online survey (</mark> _<mark>p</mark>_ <mark>) and calculate weights for the online survey observations as</mark> _<mark>(1-p)/p</mark>_ <mark>. The GMD is weighted by its own survey weights. Theoretically, the weighted means of variables used in the model should match between the online surveys and the GMD (Li et al, 2018).</mark> <mark>We also test whether non-model variables match across the datasets. For this, we re-estimate the models after dropping the original model variables one by one. Using these restricted models, we predict the probability of participating in the online surveys (p), as before. Applying the weights as before, we compare the means of the dropped variable, and calculate the relative mean deviation of the Internet survey mean (for each wave) from the GMD mean.</mark>

# **<mark>5. Results</mark>**

# **<mark>5.1. Results from Reweighting</mark>**

<mark>This section presents the weighted averages of model variables based on propensity score weights obtained from the logit models in each country. Appendix tables A.4.1 to A.4.6 present the weighted averages from GMD (column 1), online survey wave-1 (column 2), online survey wave-2 (column 3), and online survey wave-3 (column 4), along with the confidence intervals. Results indicate that we do not reject the null that the GMD means are the same as the online</mark>

> 22 Clustering usually provides more conservative standard error estimates compared to robust standard errors. Still, we
Transformation for Prosperity and Livability</mark>_ <mark>. Washington, DC: World Bank.</mark>

- <mark>______. 2016. World Development Indicators (database). Washington, DC. http://data.world bank.org/data‐catalog/world‐developmentindicators.</mark>

- <mark>Yu, Le, and Peng Gong. 2012."Google Earth as a Virtual Globe Tool for Earth Science Applications at the Global Scale: Progress and Perspectives."</mark> _<mark>International Journal of Remote Sensing</mark>_ <mark>33 (12): 3966‐86.</mark>

- <mark>Zhou, Y., S. J. Smith, C. D. Elvidge, K.  Zhao, A. Thomson, and M. Imhoff. 2014. “A Cluster‐Based Method to Map Urban Area from DMSP/OLS Nightlights.”</mark> _<mark>Remote Sensing of Environment</mark>_ <mark>147: 173–85.</mark>

32
* ) are measures for each amenity type (see Appendix C.4), and ν _i_ are their marginal valuation, to be estimated. We simultaneously estimate the system of equations by maximum likelihood and recover parameters _{_ β _, q_ 0 _,_ ν _i}_ . Appendix C.4 presents the procedure in more detail.

Third, we calibrate the disutility parameters of informal housing ( _BIB_ and _BIS_ ) by running the entire model to replicate the share of households in informal settlements and backyard housing in the 2011 Census data.

> 34Restrictions include areas used for formal subsidized housing, informal settlements, protected natural areas, large economic or industrial infrastructures (such as the Cape Town airport). A detailed list of sources is presented in Appendix B.3.

18
forcement following the terrorist attacks of 2001.<sup>1</sup> Ever since, deportations to El Salvador have maintained steady growth rates, reaching almost 50,000 individuals per year by 2018.

In principle, repatriation inflows can be understood as a pure labor supply shock as more workers enter the labor market. However, 76 percent of deportees to El Salvador spend less than one month abroad. Thereby, their effects are more likely to reflect the experience of detention, including, for example, trauma, potentially being exposed to individuals with criminal backgrounds, and being stigmatized since the reason for deportation is unknown by others back home. At the same time, repatriation inflows can represent a labor demand shock if deportees are more likely to engage in crime or other illegal activities; or if they are likely to join the informal sector, thereby affecting labor demand among formal firms facing increased competition from informal ones. Following this logic, we first examine the effects of repatriation inflows as a _supply shock_ , evaluating its effects on firm wages and employment. In a second step, we study the effects of repatriation inflows on outcomes that could be more directly linked to the _demand shock_ , including firm entry and growth (measured as number of branches), as well as increased competition from the informal sector.

We carry out our empirical analysis combining data from El Salvador’s migration authorities that include all registered repatriations with rich socioeconomic information about deportees with administrative information from the Salvadoran Social Security Institute ( _Instituto Salvadore ̃no de Seguridad Social_ , ISSS) containing the universe of firms and workers in the formal sector. Our period of analysis covers the years 2010 to 2018 and employs municipal and annual variation.

Considering that repatriated migrants may choose their location depending on local economic conditions, or that their location may have been forced on them by migration authorities, we cannot simply estimate a linear regression of firm outcomes on repatriation inflows. To account for these possible biases we use a shift share instrument that exploits two sources of variation
stimulate licensing revenue in the USA include the combined investment of university, federal and corporate R&D programs and grants, the prevalence of technology parks and incubators, venture capital and seed-expansion, as well as support services for start-ups in accounting, law, and marketing. The diversity of actors and support services results in thick markets for technology commercialization, enabling individuals to leverage the multiple institutional, financial and human resources in which they are embedded. Actors can rely on this ecosystem to seek help in negotiating flexible license terms, developing business models, recruiting talent, securing funds, and partnering with developers. The role of government support should not be underestimated. In 2010 alone, there was $39.1 billion in federally funded sponsored research expenditures in the USA (AUTM 2010). In contrast, the use of licensing in the developing world is frustrated by thin markets for technology. First and foremost, adequate IP enforcement is necessary in order for the IP system to act as an enabling environment for generating more licensing revenue. Companies must be assured that, if they pay for licenses, others will not simply steal technology, putting them at a competitive disadvantage. Yet, enforcement must be pursued in tandem with a more comprehensive approach to technology transfer, which recognizes the importance of technology parks, venture capital and monetization support services.

The existence of professional associations that collect information relating to technology transfer and reflect on the process and impact of commercialization is tremendously valuable. The Association of University Technology Managers, initially founded in 1974 in order to address concerns that US federally-funded inventions were not commercialized effectively, is now a global network of technology transfer professionals from over 350 universities, research institutions, teaching hospitals, government agencies and companies. It has been at the forefront of funding, compiling, and publishing academic technology transfer data, particularly in North America. The AUTM STATT database is a statistical resource for policy makers to understand the extent and impact of licensing programs so far. In the USA in 2010, AUTM member organizations executed