rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7b. Transparency and Governance**

_DATA SET: ICRG, Averaged over the period 1984-1997_

||ICRG|Corruption|Ruleof<br>Law|Bureaucracy<br>Quality|Contract<br>Repudiation|Expropriation<br>Risk|
|---|---|---|---|---|---|---|
|Incomepercapita|.*59***<br>(4.6)|.59***<br>(3.63)|.84***<br>(5.4)|.75***<br>(4.58)|.56***<br>(4.45)|.48***<br>(4.58)|
|Transparency|_-.50***_<br>(-6.39)|-.56***<br>(-5.49)|-.50***<br>(4.56)|-.55***<br>(-5.31)|-.46***<br>(-5.35)|-.430"<br>(-5.92)|
|Constant|.39|.10|-1.8|-1.03|.79|1.68*|
||(.36)|(.07)|(-1.35)|(-.75)|(.73)|(1.85)|
|#obs|94|94|94|94|94.|94|
|R__|_<br>.59|.47|.54|.50|.57|.58|
|_*indicatessignificanceat.I le_|_vel,**at. _|_05leveland_*|**_at.01leve_|_l._|||

**Table 8a. Transparency and Governance in Non-OECD Countries**

<!-- Start of picture text --> DA TA SET: KA UFMANN, KRAA Y AND ZOIDO-LOBA TON<br><!-- End of picture text -->

||How authoritie<br>rep|sareselectedand<br>laced|Capacityoft<br>implement sol|he stateto<br> ndpolicies|Respectofthe<br>citizensfo|state andthe<br>rtherules|
|---|---|---|---|---|---|---|
||Voice and<br>Accountability|Political Instability<br>andViolence|Government<br>Effectiveness|Regulatory<br>Burden|Ruleof<br>Law|Graft|
|Incomepercapita|.22***|.45***|.30***|.27***|.51**|.35***|
|<br>(1)|(2.69)|(5.61)|(4.91)|(4.61)|(8.12)|(5.62)|
|Transparency|-.12*|-.03|-.09*|-.13**|-.001|_-.05_|
||(-1.74)|(-.59)|(-1.77)|(.02)|(-.02)|(-1.1)|
|Constant|i1.57**|-3.76***|-2.44***|-I.79***|-4.29|-2.93**|
||(-2.01)|_(-5.09)_|(4.45)|(-3.27)|(-7.51)|(-5.45)|
|#obs|113|102|103|108|108|102|
|R__|_<br>.18|.33|.31|.30|.45|.34|

**Table 8b. Transparency and Governance in Non-OECD Countries**

_DATA SET: ICRG, Averaged over the period 1984-1997_ <!-- Start of picture text --> ICRG Corrupti6n Rule of Law Bureaucracy Contract Expropriation<br>Quality Repudiation Risk<br>Income per capita .27*** .19 .43*** .32** .32*** .26**<br>(2.83) (1.48) (3.68) (2.58) (2.84) (2.79)<br>Transparency -.32*** -.35*** -.26** -.32*** -.32*** -.29***<br>(4.46) (-3.8) (-2.4) (-3.24) (-3.75) (4.14)<br>Constant 2.13$* 2.32** .36 1.36 2.01** 2.82***<br>(2.69) (2.30) (.36) (1.34) (2.15) (3.56)<br>#obs 72 72 72 72 72 72<br>.34 .19 .24 .21 .32 .32<br><!-- End of picture text --> Table 9 shows the significance of the index after controlling for legal origin. Using table 9 as the standard set of regressions, several variations are run by adding different variables one at a time to check for the stability of the significance of the transparency index. The index 24
1.6 results 29

Table 1: Main Regressions: FDI Stock in 2007-2008

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Probit|Benchmark|Hetero|geneity|Bias I|Bias II|
||||NLS|OLS|||
|FDI Procedures|-.060|.458**|.514**|.501**|.515**|.457**|
||(.095)|(.197)|(.200)|(.202)|(.199)|(.203)|
|FDI Arbitration|-.159**|.260|.411**|.409**|.362*|.300|
||(.064)|(.206)|(.200)|(.197)|(.195)|(.206)|
|FDI Openness|-.113|.054|.141|.127|.119|.061|
||(.107)|(.128)|(.126)|(.130)|(.124)|(.126)|
|Quality of Institutions|.390**|-.111|-.316|-.330|-.337|-.132|
||(.153)|(.203)|(.207)|(.208)|(.212)|(.207)|
|GDP|.624***|.857***|.468***|.496***|.492***|.842***|
||(.067)|(.083)|(.124)|(.134)|(.124)|(.092)|
|GDP Per Capita|-.189*|.036|.150|.141|.143|.008|
||(.109)|(.184)|(.190)|(.185)|(.187)|(.180)|
|Weighted Tariff|-.255*|-.390**|-.026|-.221|-.232|-.387*|
||(.137)|(.196)|(.020)|(.206)|(.203)|(.196)|
|School (Mean Years)|.575***|.295|-.146|-.083|-.081|.323|
||(.175)|(.305)|(.328)|(.331)|(.324)|(.302)|
|Remoteness|2.814***|2.822***|1.251*|1.466*|1.417*|2.787***|
||(.459)|(.669)|(.682)|(.798)|(.718)|(.692)|
|Distance|-1.578***|-1.382***|-.466*|-.550**|-.534**|-1.364***|
||(.168)|(.147)|(.236)|(.264)|(.225)|(.155)|
|Contiguity|.347|1.083**|1.030**|1.066**|1.053**|1.080**|
||(.615)|(.454)|(.437)|(.427)|(.430)|(.448)|
|Colony|.632*|.938***|.544*|.605*|.578*|.937***|
||(.363)|(.311)|(.291)|(.310)|(.296)|(.306)|
|Investment Treaty|.392***|.267|||||
||(.140)|(.179)|||||
|δfrom<br>�<br>~~ˆ~~<br>ω<sup>_~~∗~~_</sup><br>ij<br>�|||.682***||||
||||(.201)||||
|Inverse Mills Ratio<br>�<br>~~ˆ~~<br>η<sup>_~~∗~~_</sup><br>ij<br>�|||.177|.003||-.179|
||||(.358)|(.600)||(.378)|
|Z1<br>�<br>~~ˆ~~<br>z<sup>_∗_</sup><br>ij<br>�||||1.175***|||
|||||(.405)|||
|H1<br>�<br>Φ<sup>−1</sup>( ˆpr)<br>�|||||.663***||
||||||(.148)||
|No.|1578|912||912|912|912|
|Adj. R-sq.||.66|.68|.67|.67|.66|
|Note1: robust standard error|s (clustered by|host country) re|ported in|parenthesi|s||
|Note2: statistical signifcance|indicated as *|forp<0.1, ** for|p<0.05, a|nd *** forp|<0.01||
from 2014 to 2016. It uses Near Field Communication (NFC) technology, which allows cards or cell phone and a payment terminal to communicate wirelessly with each other without having to enter a secret code. NFC is, in a nutshell, a technology that allows secure data exchange within a 10 cm radius. For more information on NFC technology, please refer to Egger (2013) who gives an overview of NFC technology and its applications. Initially, the limit of contactless payment was 20 euros until October 2017 where it increased to 30 euros. At the end of 2014, 46 percent of CB cards, i.e. 28 million contactless CB cards, and 20 percent of CB merchants were equipped with the CB contactless technology; overall, 64.5 million contactless CB transactions for a total amount of EUR 706.5 million were recorded by CB. Five years later, at the end of 2019, 82 percent of CB cards and 67 percent of CB merchants were contactless, for a total activity of about 3.4 billion contactless CB transactions and EUR 37.6 billion. In 2020, the world was affected by the COVID-19 pandemic. In order to halt the spread of the virus in France, the government imposed a first containment from mid-March to mid-May. To support this citizen initiative, on May 11, 2020, French banks raised the limit for contactless payment to 50 euros in order to reduce physical contact and the risk of infection, and to promote social distancing and contactless behavior. This measure was marked by an acceleration in the use of contactless payment technology. After the first billion transactions reached in 2017, then the 2 and 3 billion exceeded in 2018 and 2019, 4.6 billion contactless CB transactions were made in 2020. Nearly 60 percent of CB transactions under 50 euros were carried out in contactless stores in 2020 and 78 percent of the merchants were contactless in the end of 2020. More than half of CB payments are now made in stores using contactless cards or cell phones, demonstrating the confidence and adoption of this payment method by CB cardholders and merchants (Cartes Bancaires CB, 2021).

_COVID-19 pandemic and containment in France_ . On March 11, 2020, the World Health Organization announced that the COVID-19 epidemic was classified as a pandemic (WHO, 2020). By mid-April 2020, COVID-19 had infected more than 2 million people in 210 countries and caused more than 150,000 deaths. Countries around the world have put in place increasingly stringent public health measures to deal with the pandemic. These measures range from social distancing to complete lockdown (including closure, mobility restrictions and mandatory confinement), invariably restricting economic activities that have serious repercussions. Many governments have put in place massive fiscal stimulus packages to help combat the negative economic consequences of COVID-19. New support programs have been added as the pandemic has evolved. Unfortunately, these drastic measures led to a sharp decline in production levels in many economies, with consumer 5
**The focus of this work is to identify individuals who, through the remittance data, appear to be foreign workers or carrying out transactions on behalf of other foreign workers.** Unlike our earlier work (World Bank, 2020), this exercise does not attempt to identify individuals who appear to be irregular workers (see Annex 1 for a comparison of methodologies used). Rather, the main objective is to estimate the total number of foreign workers between March 2017 and February 2018, looking at the monthly numbers as well as the annual average size of the foreign worker population. Additionally, the current method uses the difference between the remittance analysis and lawfully-employed foreign population data from MOHA to estimate potential irregular workers. The specific steps are described below.

# **Step 1. Create a sample of potential foreign workers using objective variables of individual observations (remittance transactions).**

- (i) We restrict the sample to individual remitters between the ages of 18 and 55 years. According to Malaysia’s foreign worker system, foreign workers must be between the ages of 18 and 45 years at the time of their work permit application, and they are allowed to stay in Malaysia for up to 10 years from their entry.

- (ii) Remittances from Malaysian citizens are excluded except in cases where the remitter sends money to multiple recipients in different countries indicating employment as the source of funds, which may indicate that the transaction was done on behalf of multiple foreign workers.

- (iii) As in World Bank (2020), we exclude those individuals whose monthly remittance amount is more than US$650 (two standard deviations above the mean), as the average monthly wage of foreign workers is not more than US$650. As a point of reference, the World Bank KNOMAD surveys (2015) show that Vietnamese workers in the manufacturing sector send, on average, US$200/month to their family members back home.

- (iv) We exclude observations with transaction amounts of RM5,000 or more (equivalent to about three months’ salary of an ordinary foreign worker). The World Bank Greenback 2.0 surveys find that foreign workers send money home at least once every two months.

# **Step 2. Identify transactions undertaken on behalf of others in each month and record them as**

**transactions done by different individuals.** We treat the following transactions as those done by another individual:

- (i) The third or subsequent transactions by an individual within a month, assuming that foreign workers send money home, on average, twice a month at most, as each transaction entails remittance costs and they are sensitive to these costs.

- (ii) Second or subsequent transactions by an individual on the same day.

- (iii) Transfers to a country that is different from the remitter’s nationality.

- (iv) Transfers made by a Malaysian to various destination countries. For example, most of these cases specify that the purpose of the transfer is “family maintenance” and the source of funding as “employee salary” or “others’ income.”

8
Figure 3: Change in monthly sales <!-- Start of picture text --> (a) Relative to 2019 (b) Recovery from wave 1<br>0 0<br>-10 -20<br>-20 -40<br>-30 -28 -60<br>-80<br>-40<br>-41<br>-100<br>-50 -100 -80 -60 -40 -20 0<br>Wave 1 Wave 2 Change in sales - wave 1<br>Change in sales (%)<br>Change in sales - wave 2<br><!-- End of picture text --> _Three_ , with sales recovering relative to the initial phase of the pandemic, the expectations of future sales have improved. In the following 6 months, firms expect their sales to be, on average, only 5% below 2019 levels, which is a considerable improvement compared to 11% in wave 1 (Figure 4, panel a). Notably, firms’ expectations about future sales in wave 1 is positively correlated with their actual sales nearly six months later. Nonetheless, there is an error such that the actual sales are lower than that expected in wave 1 by 17 percentage points (see Appendix Figure C3). This expectation error is tightly correlated with the uncertainty about the future (Bloom et al., 2020b), which does not seem to have faded at all by wave 2 (Figure 4, panel b).

Figure 4: Expectations and uncertainty about sales <!-- Start of picture text --> (a) Expectations next 6 months (b) Uncertainty<br>0 30<br>-5 20 19<br>-5<br>16<br>-10 10<br>-11<br>-15 0<br>Wave 1 Wave 2 Wave 1 Wave 2<br>Std Deviations<br>Expectation about change in sales<br><!-- End of picture text -->

# **4.2 Stylized Fact 2: Business recovery is not apparent in all spheres, that is, large uncertainty still persists and firms remain financially fragile.**

Improvement in other dimensions of firm performance may be slow and the effects of the crisis could be more long-lasting. _First_ , as shown in Figure 4 (panel b), uncertainty, measured as the standard deviation in a firm’s expectations, remains high and comparable to that in wave 1.<sup>12</sup> This finding is

> 12Uncertainty is measured at the firm level as the standard deviation of each firm’s expectations under three scenarios (regular, pessimistic, and optimistic), using as weights the self-assessed probability of each scenario.

12
<!-- Start of picture text --> 3E(VL) _ [Pr(VL, > 0 )E 0 Ei E(L' 1 VL' >0)1<br>( =[ E(VL, I VL, > O)]+[Pr(VL, > 0) ( '<br>0.0098 49.39 0.3054 1.70<br>Extensive Intensive<br>Margin Margin<br>0.482 0.518<br><!-- End of picture text --> In other words, we find that roughly half (0.518) of an expansion in the VL program grants benefits existing participants, and the other half (0.482) benefits new participants. Further, for a one Sole increase in per capita VL grants, we estimate that the probability of participation increases by one percent.

# _Inframarginality of Transfers_

Further, the PETS data can give us a sense of how the in-kind VL transfers compare to equivalent cash transfers. In other words, we can get an indication of whether the quantities of milk provided to households by the program are equal to the marginal increases in the quantities of milk consumed by those households. If so the transfers are considered to be extramarginal, and are expected to have a larger impact on milk consumption than an inframarginal program might have. This follows from the general equivalence of inframarginal transfers and cash transfers of the same value. Extramarginal transfers have a price effect as well. 10 We find that nearly half of all the recipients consume additional amounts of the products distributed to them through the VL program (Table 12). For example, for the 80 percent of the recipients who receive milk and milk substitutes from the VL program, 49 percent purchase additional milk and milk substitutes. For 30 percent of the households that receive milk/dairy products, the program is inframarginal for 43 percent of them with respect to these particular products. While only 3 percent of those households that receive milk substitutes (53 percent of recipient households) purchase additional milk substitutes, most of these households also purchase milk/dairy products. In other words, for half of these households, the VL program is inframarginal over the more broadly defined category of milk and milk substitutes (hence the 49 percent figure above), but not for milk substitutes alone.

Io The transfer is extra-marginal if _ex-post_ consumption (what we observe) is exactly equal to the transfer - i.e., the recipient would consume less of the product if the transfer was in the form of cash (he/she is consuming at the kink in the budget constraint). Alternatively, if she purchases additional amounts of the product, then the transfer is infra-marginal.

_25_
# 1. _Introduction_

Historically and across countries, there has been a wide variation in the relative importance of public production. Traditionally, the share of government production in total output has been larger in developing countries than in industrialized countries, with the exception of the centrally planned economies. In recent years, there has been a strong tendency to privatize state-owned enterprises around the world, thereby reducing the share of public production. Wholesale privatization has been underway in Eastern Europe after the collapse of communism. More subtly, the United Kingdom has reduced the share of public production of goods and services for sale in GDP from 6.6 percent in 1982 to 1.9 percent in 1991, compared to a share of 0.6 percent in the United States in 1987, and a share of 10.0 in a 'statist' country like France in 1988.' Worldwide, privatization occurs in the belief that it will increase production efficiency and produce welfare gains. Some detailed evidence indeed supports this view (see Boardman and Vining (1989) for a survey).

The present trend towards privatization somewhat obscures the underlying and enduring question of what is the appropriate role of governrnent in production. In 1776 Adam Smith thought that private enterprise was the preferred mode of production. Considering the privatization of crown lands, he writes: "When the crown lands had become private property, they would, in the course of a few years, become well improved and well cultivated."<sup>2</sup> Following experiences with excessive capitalism in the 19th century in England and elsewhere, Keynes in 1927 appears to be more of a pragmatist. Considering the boundary between the private and public sectors, he remarks: "The line of demarcation between the two is constantly changing in accordance with the practical needs of the day. As to where precisely this line should be drawn no great question of principle is involved."<sup>3</sup> Current thinking largely remains pragmatic, although some principles have been enunciated. In a thoughtful essay on the role of the state, Stiglitz (1989) argues that the relative attractiveness of public ownership and production depends on the importance of market failures vs. public failures. Failures in either sector can arise on account of a lack of competition, imperfect information, and incomplete markets. A source of public failures, in addition, may be a government's inability to bind its future actions. Along these lines, several papers have formalized the choice between private and public production. Shapiro and Willig (1990), and Hart, Schleifer, and Vishny (1996) focus on the market failure of incomplete contracts; Bos and Peters (1991) consider the market failure of imperfect competition; finally, Schmidt (1996) considers the public failure of uncomritting governments. While these papers are insightful, they do not offer a workable explanation of, say, cross-country variation in the importance of public production. The 1
# **Table 3: The Real Exchange Rate and Growth of Exports across Countries (Different Income Groups)**

||**I**|**II**|**III**|
|---|---|---|---|
||**Merchandise**|**Traditional Services**|**Modern Services**|
|Log Per Capita Income, Lag|-0.03|-0.03|0.02|
||[1.14]|[1.41]|[0.34]|
|RER, percent change|0.11***|0.09**|0.17***|
||[2.80]|[2.48]|[2.90]|
|RER* developing country dummy|0.04|0.06|0.07|
||[0.73]|[1.54]|[0.97]|
|Country Fixed Effects|Yes|Yes|Yes|
|Time Fixed Effects|Yes|Yes|Yes|
|Observations|380|380|380|
|Number of Countries|66|66|66|
|R-square|0.31|0.35|0.26|

Note: *, **, *** indicate that coefficients are significant at 10, 5, and 1 per cent levels respectively. The dependent variables are growth in exports of merchandise (Column I), exports of traditional services (Column II) and exports of modern services (Column III), all in constant USD. Observations are averages over five-year periods. Data are from 1980-2009. Regressions include country fixed effects, and time fixed effect, as indicated. Standard errors are robust and clustered at countries. Log per capita income is lagged by one five year period, per cent change in real exchange rate is average of annual values over contemporaneous five years.

.

Next we allow for the effect to differ for depreciation and appreciation of the RER. In Table 4 we add a dummy variable that equals 1 when the percentage change in the real exchange rate is positive, i.e. when there is depreciation, by interacting it with the real exchange rate. In the case of merchandise exports, the effect of exchange rate depreciation is somewhat larger but does not differ significantly from the effect of appreciation. Nevertheless, the coefficient on the RER for depreciation (0.07+0.12) is significantly different from 0 at the 1 percent level of significance. In the case of traditional and modern services, appreciation does not seem to affect export growth, but the effect of depreciation is positive, large and significant. As before, the effect is larger for export of modern services than for traditional services.

In sum, while real exchange rate changes affect exports of merchandise and traditional services in broadly similar ways, the effect on modern services is larger.

**Table 4: Depreciation vs. Appreciation and Export Growth**

8
for Syrian fields and grey symbols for Iraqi ones. The black circles with labeled years report the same quantities as for Syria in total. The line shows the slope of the oil-RH relationship estimated by ordinary least squares, while the shaded area is the 95 percent confidence interval.

Figure 2: Radiant heat and oil output <!-- Start of picture text --> 2012<br>2013<br>20152014<br>-6 -4 -2 0 2 4<br>Log(radiant heat)<br>95% confidence interval Point estimate<br>Syria Fields Iraq Fields<br>Syria total<br>5<br>0<br>Log(oil output) -5<br>-10<br><!-- End of picture text --> Beta=1.0165 Linearity test (p-value) = 0.8937 R-squared=0.7311 Note: Plot of linear regression of the logarithm of field RH against the logarithm of field oil output on the vertical axis. Each point represents an oil field annual average unless otherwise labeled. Linear regression and 95% confidence intervals also depicted. The fail to reject the null hypothesis that the slope equals one (see Annex A section A.4 for details on estimation sample). Data sources: [45, 46, 24] and NOAA.

Under the assumption that the oil-RH relationship estimated using data prior to the seizure of oil fields by Daesh still holds thereafter, we can make consistent statistical inference about _contemporaneous_ volume of crude oil extracted from _contemporaneous_ measures of RH.

7