rafmacalaba/datause-encoder-data huggingface dataset:
- 24 -

are in the majority. There is also an extreme polarization based on the final selling price of the firm - with the investment funds having clear majorities in medium and high price firms and individuals dominating in low price firms. This pattern would seem to reinforce the greater likelihood of success of enterprises that sold for relatively higher prices. Non-coupon shareholders in the 1491 enterprises are also distributed very unevenly across Republics and

|Table 6: Governance: Average Di<br>of Enterprise Ownership<br>in the Coupon Schene<br>Coupon Sche<br>Individuals|stribution<br> <br>me<br>IPFs|
|---|---|
|Total<br>48.6|51.4|
|Czech<br>453|547|
|<br>.<br>Slovak<br>55.1<br> <br>|.<br>44.9|
|Low Price<br>56.6|43.4|
|Medium Price<br>42.7|57.3|
|high Price<br>47.7|**_52.3_**|

across enterprises of different prices. On average, Czech firms have much larger shareholdings by foreign and domestic investors, restitution claims, bank and local authorities. In contrast, governance in Slovak firms is far more diffuse on average. In fact, such diffuse ownership by individual coupon holders may result in de facto management control, more along the lines of so-called 'spontaneous privatization" in other countries. Similarly, high price firms also have a greater proportion of their shares held by significant noncoupon interests relative to low price firms. Thus the initial prospects for corporate governance are far more favorable in Czech firms and in firms that sold for higher prices.

In many countries, there is a strong tendency for share ownership to quickly become concentrated after the initial dispersion of ownership caused by mass privatization.' It is not surprising that many individuals participate in the process to take advantage of the transfer element, which they are keen to convert into a cash transfer as soon as possible. Thus those that are interested in actually managing the firm tend to buy 'out those who participated in order to take advantage of the public transfer. However, in Czechoslovakia, this process of concentration has
two different crops, wheat and rice, have led to persistent differences in culture, which are reflected in the mental models that individuals draw upon, their perception, and their behavior.

Behavioral economics is replete with examples of how context and framing matter. The effects have been demonstrated in laboratory experiments, in the field, and recently even in policy. They are generating extensive replication to test the generality of the underlying mechanisms and the possibility of scaled-up interventions.

A field experiment in Kenya (Dupas and Robinson 2013) has results consistent with those of the money prime laboratory experiment. All the subjects received information about preventative health products, and all were encouraged to save in order to purchase them. Each individual in a randomly chosen group was given two additional things: a lockable box and a passbook to label with product that the individual hoped to purchase with his savings. Having the box and passbook had large effects on behavior. The proportion of people who reached their savings goals was only one-third in the control group (without the box and passbook but with the encouragement) but one-half for those who also had the box and passbook. The amount of spending on preventative health products in the past 12 months was 66-75 percent higher for those with the lockable box compared to those with only verbal encouragement to save for health needs.

How did the subjects manage to increase their spending on health products? Post-interview surveys suggest that the lockable box and labelled passbook helped the individuals _mentally_ allocate money for savings.<sup>22</sup> The box and passbook also made the individuals feel less obligated to share available money with others in need. A survey revealed that the group with the lockable box felt much less obligated than those without the box to share with others when their only available cash was in the box.<sup>23</sup> The mental accounting changed the way the subjects addressed both temptations to spend and pressures to share.

# 3.2  Social context and priming

Behavior is often best described _as if_ individuals had multiple identities: distinct sets of beliefs, preferences, and behaviors. Social context affects which identity is most salient. The model of

> 22 This result is consistent with the “mental models” analysis presented in sections 4 and 5.  It is also consistent with the results of a field experiment in India that seemingly inconsequential actions—earmarking a portion of weekly earnings for savings by putting it into a separate envelope, and partitioning the earmarked funds into two envelopes—increased savings (Soman and Cheema 2011).

> 23 Dupas and Robinson, pp. 1164-65.

21
# **Literature**

Allen, J. and E. de Weert (2005): _What do educational mismatches tell us about skill mismatches? A cross-country analysis_ . Paper for the seminar European Labour Market of Higher Education Graduates: Analysis of the CHEERS Project, Segovia, June 17-18.

Altonji, J.G. and R.M. Blank (1999): Race and Gender in the Labor Market. In: Ashenfelter, O. and D. Card (eds.) _Handbook of Labor Economics_ , Vol. 3.

Angrist, J.D. and A.B. Krueger (1999): Empirical Strategies in Labor Economics. _Handbook of Labor Economics_ , 3, Chapter 23:1277-1366.

Baker M. and Benjamin, D. (1994) “The Performance of Immigrants in the Canadian Labor Market,” _Journal of Labor Economics_ , 12: 369-405.

Battu, H. and P.J. Sloane (2004): Overeducation and Ethnic Minorities in Britain. _The Manchester School_ , 72(4):535-559.

Bauer, T. (1999): _Educational mismatch and wages in Germany_ . IZA, Discussion Paper no. 87.

Becker, G.S. (1971): _The Economics of Discrimination_ . Chicago: The University of Chicago Press.

Belzil, C. and J. Hansen (2002): _A Structural Analysis of the Correlated Random Coefficient Wage Regression Model_ . IZA Working Paper.

Borghans, L. and A. de Grip (eds.) (2000): _The Overeducated Worker? The Economics of Skill Utilization_ . Cheltenham: Edward Elgar.

Borjas, G.J. (1995) “Assimilation and Changes in Cohort Quality Revisited: What Happened to Immigrant Earnings in the 1980s?” _Journal of Labor Economics_ , 13: 201-245.

Borjas, G.J. (1994) “The Economics of Immigration”, _Journal of Economic Literature_ , 32(4):1667-1717.

Borjas, G.J. (1987) “”Self-Selection and the Earnings of Immigrants” _The American Economic Review_ , 77(4): 531-553.

Borjas, G.J. (1985) “Assimilation, Changes in Cohort Quality, and the Earnings of Immigrants” _Journal of Labor Economics_ , 3(4): 463-489.

Böhlmark, A. (2003): _Over- and Undereducation in the Swedish Labour Market. Incidence, Wage Effects and Characteristics 1968-2000_ . Swedish Institute for Social Research (SOFI), Stockholm University.

30
# **MOBILE INFRASTRUCTURE & RURAL BUSINESS ENTERPRISES: EVIDENCE FROM SIM REGISTRATION MANDATE IN NIGER**

Annan, Francis & Sanoh, Aly<sup>1</sup> Poverty & Equity Global Practice World Bank Group Key words: mobile infrastructure; regulatory change; instrumental variable; business enterprises; poverty JEL classification: H54; O12; O18; I38; I32

> 1 **Annan** : `fa2316@columbia.edu` & `fannan@worldbank.org` . **Sanoh** :

`asanoh@worldbank.org` . For helpful discussions and suggestions, we thank Andrew Dabalen; discussant Tawanda Chingozha and seminar participants at the “Poverty and Inequality” session of 2017 CSAE Conference in Oxford. All remaining errors are ours.
**Policy Research Workilng Paper Series**

||**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|---|
|WPS883|Malaria: The Impact of Treated Bed-<br>Nets on Childhood Mortality in the<br>Gambia|Pedro L. Alonso<br>Allan G. Hill<br>Patricia H. David<br>Greg Fegan<br>Joanna R. M. Armstrong<br>Andreas Francisco<br>K. Cham<br>Brian M. Greenwood|April 1992|0. Nadora<br>31091|
|WPS884|Intercommodity Price Transmittal:<br>Analysis of Food Markets in Ghana|Harold Alderman|April 1992|C. Spooner<br>30464|
We employed two data sources. First, thanks to the collaboration with the Tunisian National Institute of Statistics (INS), we rely on the _Enquête Nationale sur la Population et l’Emploi_ (ENPE), which provides nationally representative socioeconomic information at the individual level from 2000 to 2019. The ENPE is conducted by the INS and consists of demographics and labor market data. The survey consists of repeated cross-sections and does not allow to track individuals over time. However, it includes rich information and represents a valuable source of data to analyze the labor market in Tunisia. We started with an initial sample at the individual level across 24 governorates. After restricting the sample to working age individuals (between 15 and 65 years old), we collapsed the data into governorate x year cells to obtain a balanced panel of yearly outcomes of the labor markets.

Exploiting the details of employment status and the economic sector codes (NACE classification) for employed individuals, we focused on four labor market outcomes at the governorate level: the share of employment in the agriculture sector, the share of employment in less climate sensitive sectors (namely, manufacturing and construction), the share of employment in all the other economic sectors (excluding the public sector), and the unemployment rate.

Other information includes the shares of the population across five age groups; the gender of the workforce; the share of educational levels across the population; and the share of the workforce population living in urban areas. We aligned ENPE socioeconomic data with detailed weather information that we obtained from the Agri-4-Cast database. These data are provided by the Food Security Unit of the Joint Research Center (JRC.D.5) and were specifically employed for identifying the climate change impacts in the agriculture sector. The data consists of gridded meteorological observations from weather stations interpolated on a 25 × 25 km grid. They are available on a daily basis from 1979 in the European Union and its neighboring countries, including Tunisia. We selected variables for minimum and maximum air temperatures (in Celsius degrees) and sum of precipitation (mm/day) to calculate the Standardized Precipitation Evapotranspiration Index (SPEI), our main treatment measure. The SPEI represents a stateof-the-science indicator for measuring the impact of increased temperatures on water demand (VicenteSerrano et al., 2010; Chiang et al., 2021). Since climate grids come at a finer spatial resolution than our administrative unit of analysis, we calculated SPEI values for each grid point falling within each administrative unit. We then weighted the SPEI value of each grid data point with the resident population.

Finally, we collapsed the data to obtain medians of population-weighted SPEI values in each governorate 6
Table 4. Impacts of the PEP Program on Crime Reports by Migrant’s Gender

|Male Total Crimes|Female Total Crimes|
|---|---|
|(1)|(2)|
|_Panel A. OLS - Crime Reports by Venezuelans (ratesper 100,000)_||
|_PEPc × I_[_PostAugust_2018]<br>0.023|0.109***|
|(0.020)|(0.026)|
|FDR q-values<br>[0.143]|[0.001]|
|bootstrap p-val.<br>[0.314]|[0.000]|
|Crime Reports Tot<br>0.089|0.407***|
|(0.069)|(0.088)|
|AdjR-squared<br>0.526|0.553|
|_Panel B. 2SLS - Crime Reports by Venezuelans (ratesper 100,000)_||
|_PEPc × I_[_PostAugust_2018]<br>0.014|0.159***|
|(0.029)|(0.042)|
|FDR q-values<br>[0.442]|[0.001]|
|bootstrap p-val.<br>[0.716]|[0.002]|
|Crime Reports Tot<br>0.085|0.430***|
|(0.067)|(0.088)|
|Adj R-squared<br>0.526|0.550|
|KP F Stat<br>21.46|21.46|
|_Panel C. OLS - Crime Reports by Venezuelans (ratesper 100,000) -_|_Excluding Controlsfor Total Crime_|
|_PEPc × I_[_PostAugust_2018]<br>0.020|0.096***|
|(0.019)|(0.027)|
|FDR q-values<br>[0.168]|[0.003]|
|bootstrap p-val.<br>[0.345]|[0.000]|
|AdjR-squared<br>0.525|0.543|
|_Panel C. 2SLS - Crime Reports by Venezuelans (ratesper 100,000)_|_- Excluding Controlsfor Total Crime_|
|_PEPc × I_[_PostAugust_2018]<br>0.012|0.149***|
|(0.028)|(0.044)|
|FDR q-values<br>[0.484]|[0.003]|
|bootstrap p-val.<br>[0.709]|[0.005]|
|Adj R-squared<br>0.525|0.541|
|KP F Stat<br>21.21|21.21|
|Observations (All Panels)<br>1,080|1,080|

_Notes:_ The table presents the OLS and 2SLS estimation of specification (1). All columns include city and month-year fixed effects as well as the set of controls described in section IV. Standard errors are clustered at the city level. Cluster-robust wild-bootstrap p-values, and FDR q-values are presented in square brackets. To ensure statistical rigor, we consider a coefficient statistically significant only if it remains valid after adjusting for multiple hypothesis testing and accounting for the small number of clusters.

Finally, we estimate the program’s effects on crimes reported by victims of Colombian nationality.

24
# **Section 1**

OLS regression results in this section are based on the following equation _S i_ = α + β _Institutionsi_ + β 1 _Geographyi_ + β 2 _Infrastructurei_ + β 3 _X i_ + _u i_ ... (1) where _i_ denotes country, _S_ is (logged) value added in the service sector in 2000 expressed as percentage of GDP in constant 2000 USD and PPP adjusted.<sup>6</sup> The _Institutions_ variable captures the level of institutional development in a country and is proxied by the Rule of Law index (henceforth, _Law_ ) as discussed above. _Geography_ includes the _Latitude_ or the absolute distance of country from equator and _Continent_ which is a set of dummy variables indicating the region (or continent) to which a country belongs. There are six regions: _Africa_ , _Asia_ , _Europe_ , Latin America and Caribbean ( _LAC_ ), _North America_ and the residual category called _Others_ . The _Infrastructure_ variable includes per capita generation of electricity ( _Power_ ) and per capita road length ( _Roads_ ). _X_ is a vector of additional controls including population ( _Pop_ ), per capita GDP ( _GDP_ ), PPP adjusted and in constant 2000 USD, and an endowment variable ( _Skill_ ) which is the stock of tertiary educated (per capita) in the country taken from Barro and Lee dataset. Unfortunately, data on _Skill_ is missing for about 30% of the countries in our sample and hence regressions using _Skill_ are reported separately. We use logged values of all variables other than _Law_ and _Latitude_ . Values of _GDP_ , _Pop_ , _Power_ and _Roads_ relate to year 2000

> 6 To smooth out temporary annual fluctuation we use the average values for _Si_ over 1999-2001 period.

7
# **Figure 2: The distribution of consumption conditional on selected circumstance variables**

<!-- Start of picture text --> a. By mother’s education<br><!-- End of picture text --> <!-- Start of picture text --> Colombia Ecuador Guatemala<br>-2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2<br>consumption consumption consumption<br>none incomplete none incomplete none incomplete<br>primary complete primary complete primary complete<br>Panama Peru<br>-2 -1 0 1 2 -2 -1 0 1 2<br>consumption consumption<br>none incomplete none incomplete<br>primary complete primary complete<br>b. By ethnicity<br>Colombia Ecuador Guatemala<br>-2 -1 0 1 2 -2 -1 0 1 2 -2 -1 0 1 2<br>consumption consumption consumption<br>minority others minority others minority others<br>Panama Peru<br>-2 -1 0 1 2 -2 -1 0 1 2<br>consumption consumption<br>minority others minority others<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text --> 47
**Table 12: Effect of disclosure by firm characteristic, between firm**

||**All categories**|**Disclosure**<br>**categories only**|**Child labor**|**Contracts**|**Discrimination**|**Forced**<br>**Labor**|**Freedom of**<br>**association**|**Occupational safety**<br>**and health**|**Work time**|
|---|---|---|---|---|---|---|---|---|---|
|POST|‐0.037***|‐0.031|‐0.064***|0.150***|‐0.005**|‐0.004|‐0.143***|‐0.070***|0.003|
||(0.005)|(0.024)|(0.021)|(0.013)|(0.002)|(0.003)|(0.013)|(0.009)|(0.016)|
|Firm size|‐0.014***|‐0.009|‐0.049***|‐0.030***|‐0.001**|0.000|‐0.009***|‐0.016***|‐0.024***|
||(0.001)|(0.006)|(0.006)|(0.005)|(0.001)|(0.000)|(0.003)|(0.003)|(0.005)|
|Firm age|0.002|0.005|‐0.012**|‐0.002|‐0.001*|0.000|0.003|0.008***|‐0.009*|
||(0.001)|(0.006)|(0.006)|(0.005)|(0.000)|(0.000)|(0.003)|(0.003)|(0.005)|
|Baseline|0.050***|0.028***|0.077***|0.081***|0.002***|0.000|0.025***|0.083***|0.056***|
|noncompliance|(0.001)|(0.006)|(0.006)|(0.005)|(0.001)|(0.000)|(0.003)|(0.003)|(0.005)|
|Length of|‐0.043***|‐0.033***|‐0.044***|‐0.087***|0.000|0.000|‐0.028***|‐0.064***|‐0.057***|
|participation|(0.002)|(0.006)|(0.006)|(0.006)|(0.001)|(0.000)|(0.003)|(0.003)|(0.006)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|No|No|No|No|No|No|No|No|No|
|Observations|209150|3004|7465|20671|48001|12016|24032|77959|17504|
|R‐squared|0.015|0.031|0.058|0.05|0.001|0.002|0.029|0.025|0.023|

Note: Non‐compliance=1. Firm size, firm age, baseline non‐compliance, and length of participation in program are all identified as a dummy variable equal to 1 if the firm is above the median. Standard errors in parentheses. * = 10% significance, ** = 5% significance, *** = 1% significance. Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

26
Policy Research Working Paper 10911

# **Abstract**

The arrival of cloud computing provides firms a new way to access digital technologies as digital services. Yet, capital incentive policies present in every OECD country are still targeted towards investments in information technology (IT) capital. If cloud services are partial substitutes for IT investments, the presence of capital incentive policies may unintentionally discourage the adoption of cloud and technologies that rely on the cloud, such as artificial intelligence (AI) and big data analytics. This paper exploits a tax incentive in the UK for capital investment as a quasi-natural experiment to examine the impact on firm adoption of cloud computing, big data analytics and AI. The empirical results find that the policy increased investment in IT capital as would be expected; but it slowed firm adoption of cloud, big data and AI. Matched employer-employee data shows that the policy also led firms to reduce their demand for workers that perform data analytics, but not other types of workers.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at timothy.destefano@georgetown.edu, nick.johnstone@iea.org, richard.kneller@nottingham.ac.uk, and jtimmis@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
complaints, most DPAs expect budgets to remain static in the upcoming year.<sup>169</sup> In response to these trends, the European Parliament has called for infringement proceedings against Member States accused of breaching Article 52 of the GDPR by failing to provide a budget that fosters effective performance.<sup>170</sup>

# 2020 DPA Budgets in Millions of Euro

<!-- Start of picture text --> United Kingdom 61M<br>Germany (Länder) 58.9M<br>Italy 30.1M<br>Germany (Federal) 26.8M<br>France 20.8M<br>Netherlands 18.6M<br>Ireland 16.9M<br>Spain 16.5M<br>Sweden 10.3M<br>Poland 9.4M<br>Belgium 9M<br>Czech Republic 6.7M<br>Luxembourg 6.7M<br>Denmark 5.6M<br>Finland 4.5M<br>Hungary 4.4M<br>Greece 3.1M<br>Portgual 2.4M<br>Austria 2.3M<br>Slovenia 2.3M<br>Slovakia 1.9M<br>Lithuania 1.6M<br>Bulgaria 1.4M<br>Croatia 1.3M<br>Romania 1.3M<br>Latvia 1.2M<br>Estonia 800K<br>Malta 600K<br>0 20,000,000 40,000,000 60,000,000 80,000,000<br><!-- End of picture text -->

**171**

Data subjects and related organizations submit complaints, and data processors and controllers submit data breach notifications, to the Data Protection Authorities through online forms and supplementary guided procedures. Cases with cross-border components can be received through a DPA’s website or through the Internal Market Information System (IMI), which operates as a communication tool for all EU Member States. Through IMI, DPAs can cooperate with the authorities of other concerned or lead Member States by utilizing a series of pre-translated question and answer

> 169 Estelle Massé, Two Years Under the EU GDPR, Access Now (2020).

> 170 _Id_ . at 12.

> 171 European Data Protection Board, _Individual replies from the data protection supervisory authorities_ (2020). Slightly different budget figures are reported in _Deloitte, Report on EU Data Protection Authorities Part 4: Resources_ , 2019.

29
# **TABLE A16:  Heritage Foundation (HER)**

## **A16: Heritage Foundation**

http://www.heritage.org The Heritage Foundation is a research and educational institute whose mission is to formulate and promote conservative public policies. The Heritage Foundation was established in 1973.

In 1995 the Heritage Foundation, in partnership with the Wall street Journal, launched its annual Index of Economic Freedom. This index covers 161 countries and measures economic freedoms and prospects for growth in the global economy. The index is designed for cross country research and is intended to assist international investors and aid donors in the allocation of their resources. This index is based on a detailed assessment of 10 different factors, including foreign investment codes, taxes, tariffs, banking regulations, monetary policy, and the black market. For some of these, assessments are mechanically based on objective data, while others are generated as subjective ratings based on a pre-specified checklist.

In the table below we list the variables included in each of the governance indicators. In this paper, we use Heritage data for 1996, 1998, 2000, and 2002-2005.

|**Table A16: Heritage Foundation: Index of Econ**|2005<br>**omic Freedom(161 developed**|2004<br>**and de**|2003<br>**velopi**|2002<br>**ng co**|2000<br>**untrie**|1998<br>**s)**|1996|
|---|---|---|---|---|---|---|---|
|**Voice and Accountability**||||||||
|NA|..|..|..|..|..|..|..|
|**Political Stability**||||||||
|NA|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||
|NA|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||
|Regulation|X|X|X|X|X|X|X|
|Government Intervention|X|X|X|X|X|X|X|
|Wage/Prices|X|X|X|X|X|X|X|
|Trade|X|X|X|X|X|X|X|
|Foreign investment|X|X|X|X|X|X|X|
|Banking|X|X|X|X|X|X|X|
|**Rule of Law**||||||||
|Black market|X|X|X|X|X|X|X|
|Property Rights|X|X|X|X|X|X|X|
|**Control of Corruption**||||||||
|NA|..|..|..|..|..|..|..|

66