rafmacalaba/datause-encoder-data huggingface dataset:
|Study|Control<br>Timing Mean|Effect si<br>with95%|ze<br> CI|Weight<br>(%)|
|---|---|---|---|---|
|Short-run effects|||||
|Comoros|-4m<br>26.03|6.28[ 4.16,|8.40]|7.01|
|Céte d'Ivoire|-3m<br>177.32|HE 112.10[89.19,|135.01]|6.13|
|Laos|-2m<br>4.87||<br>61.56[56.70,|66.42]|6.97|
|Djibouti|Om<br>13.87||<br>28.79[19.21,|3837]|6.84|
|Heterogeneity:t?=19|93.91,I?=99.58%,H?=237.02|—<—_-<br>51.27[7.07,|95.47]||
|<br>Testof6;=8,:Q(3)=|<br>492.82,p =0.00||||
|<br>Testof6= 0: z=2.27,|<br> <br>p= 0.02||||
|Medium-run effects|||||
|SierraLeone|Im = 77.52|i<br>27.27[16.31,|38.23]|6.79|
|EgyptCommunity|Im<br>15.51|7.46 [ 4.64,|10.29]|7.00|
|Egypt Infrastructure|4m<br>264.91|8.85 F17.49,|35.19]|5.89|
|Djibouti|9m=:10.62|-2.11[-4.44,|0.22]|7.01|
|Tunisia|<br>12m<br>24.04|.oF<br>28.32[10.54,|46.10]|6.45|
|RuralDRC|12m<br>30.11|<br><br>11.30[<br>5.44,|17.16]|6.95|
|Ethiopia|12m 166.59|a<br>21.62[<br>7.23,|36.01]|6.64|
|Cote d'Ivoire|12m 179.97|+<br>18.05[ 2.59,|33.51]|6.58|
|UrbanDRC|25m<br>56.28|1.12 [-7.45,|9.69]|6.88|
|Egypt Community|27m<br>57.41|-9.72 F18.85,|-0.59]|6.86|
|Tunisia|66m 100.21|-<br>-32.20 £56.74,|-7.66]|6.01|
|Heterogeneity: tT? = 19|0.54, I?= 94.94%, H? = 19.78|7.75 [-1.32,|16.82]||
|Test of 0;= 8,: Q(10) =<br>Testof0= 0: z= 1.67,|88.16,p = 0.00<br><br>p= 0.09||||
|Overall||><br>19.03[3.10,|34.96]||

Overall Heterogeneity: tT? = 941.67, I? = 99.25%, H? = 133.28 Test of 0; = ,: Q(14) = 687.97, p= 0.00 Test of0 = 0: z = 2.34, p= 0.02 Test of group differences: Q,(1) = 3.57, p = 0.06

-60-40-20 0 20 40 60 80100120140

Random-effects REML model
9

# **111.2 Fiscal implications of duty-free electronic commerce**

We have seen that if international trade in digitizable products shifts from a physical to electronic form because of duty-free treatment for the latter (and not because the latter is a more efficient means of delivery), then the loss in tariff revenue is a measure of the fiscal costs of trade diversion. Ideally we should calculate the tariff revenue that could be raised from aggregate imports of products for which electronic means are less efficient than alternative means of delivery. But there are serious empirical difficulties in undertaking this exercise. First, it is not possible to measure the trade that has already switched to electronic means to avoid customs duties. Secondly, it is not easy to establish product-byproduct whether electronic means are more or less efficient than the alternative means of delivery - a judgement that is in any case bound to change with technological developments. We choose therefore to determine the maximum fiscal costs of future trade diversion by calculating the total tariff revenue currently raised by countries from all digitizable products.

Since detailed and comprehensive tariff revenue data are not available, we need to estimate the tariff revenue countries collect from these products.<sup>7</sup> Table 3 provides an estimate of the weighted average tariff rates applied to digitizable products currently traded as goods, the import values for these products, and the estimated tariff revenue for various countries. The estimates take into account duty-free treatment of intra-EU, intra-NAFTA and Australia-New Zealand trade, and the reduced rates for intra-MERCOSUR trade. However, they are still likely to overestimate tariff revenue as they do not take into account other tariff reductions or exemptions.

Table 3 indicates that the average applied tariff is below 10 per cent in most countries. Of the countries included in the table, only Thailand, Morocco, Korea and India apply tariff rates above 20 per cent. Total estimated tariff revenue, therefore, adds up only to about US$ 732 million for the world as a whole8 The EU and Korea are estimated to collect close to half of the total. No other country collects more than US$ 100 million, and many below US$ 10 million.

> 7 The estimates are reasonably reliable for the most important categories where trade and tariff data were available for the most important countries. A few data problems persist as volume data for some products facing specific tariffs were not available, sometimes the tariff rate was not provided, and applied tariff rates for some of the smaller countries were not available. For an independent attempt to measure the revenue implications of electronic commerce, see Teltshcher (2000).

> 8 Not counting China, for which estimates did not seem reliable.
Zingales, 1994). In this paper, we focus on how legal protection of minority shareholders, bank supervisory and regulatory practices, and ownership concentration interact to influence bank valuations. Most directly, our paper extends LLS’s (1999) examination of corporate ownership around the world and LLSV’s (2000) examination of corporate valuations to the case of commercial banks while also assessing the impact of bank supervisory and regulatory policies on bank valuations The paper also contributes to the debate on banking sector policies. Basic theories of regulation suggest that if small stakeholders lack the means to monitor banks, then government supervision can improve welfare (Atkinson and Stiglitz, 1980). But theory also suggests that government agencies will act in their own interests, not necessarily in the interests of society (Becker and Stigler, 1974; Stigler, 1972, 1975). Empirically, Barth, Caprio, and Levine (2004) find that bank development is (a) positively associated with policies that empower private monitoring and (b) negatively associated with powerful supervisory agencies. They also find no evidence that powerful supervisors promote bank stability. Furthermore, Beck, Demirgüç-Kunt, and Levine (2003) find that (a) firms in countries with powerful supervisory agencies tend to face greater external financing obstacles, but (b) national policies that empower private monitoring of banks ease corporate financing obstacles.<sup>5</sup> This paper contributes to the debate on which public policies enhance the governance of banks.

Section 2 discusses the data. Section 3 analyses bank ownership around the world. Section 4 examines bank valuations. Section 5 provides extensions and robustness tests and Section 6 concludes.

> 5 LLSV (2003) find that securities laws that empower private agents work better than official disciplinary powers.

7
**_Deposit Insurance and Market Discipline._** In high-transparency environments, depositors discipline banks that engage in excessive risk-taking by demanding higher deposit interest rates and/or moving balances to safer institutions. Because and to the extent that deposit insurance reduces a depositor’s stake in monitoring and policing bank capital and loss exposures, it shifts responsibility for assuring transparency and controlling bank risk-taking to the regulatory system.

Of course, even if a country’s safety net covered all bank balances, depositors would remain at risk for the opportunity costs of claiming and reinvesting the amounts they are due and also for costs occasioned by delays in receiving deposit-insurance disbursements. This means that government guarantees never completely extinguish market discipline. Still, stability can be undermined if deposit-insurance managers displace more discipline than they are able to exert Using a bank-level dataset covering 43 countries over 1990-1997, Demirgüç-Kunt and Huizinga (2004) study depositor discipline by modelling deposit interest rates. They show that explicit insurance does lower a bank’s interest expense and does make interest payments less sensitive to individual-bank risk and liquidity.

It is important to investigate how particular DI design characteristics affect bank risk-taking incentives. Statistical research establishes that, although market discipline increases with institutional development, particular deposit-insurance design features consistently strengthen or weaken market discipline. On the one hand, market discipline is enhanced by coinsurance provisions, covering accounts denominated in foreign currency, and involving private managers in the insurance enterprise. On the other hand, 9
# **Table 9. Instruments for Potentially Endogenous Variables**

||Agent<br>Bank|Managemrent<br>Change|Political<br>affiliation<br>ofunions|Pre-<br>privatization<br>profits|Law<br>I/<br>ongin|Continental<br>2/<br>dummies|Macro<br>controls|<br>F-statistic<br>3<br>onexluded<br> <br>isrnnts|
|---|---|---|---|---|---|---|---|---|
|Lownsizing|yes||yes|yes|yes|yes|yes|4.32<br>|
|Voluntarydownsizing|yes|yes||yes|yes|yes|yes|3.88|
|Age-biaseddownsiang||yes|yes|yes|yes|yes|yes|3.72|
|Skill-biased downsizing||yes|yes|yes|yes|yes|yes|3.23<br>|
|Fermle-biaseddownsizing||yes|yes|yes|yes|yes|yes|3.09|
|Enploynrntguarantee|yes||yes|yes|yes|yes|yes|3.18<br>|
|Paycut|yes||yes|yes|yes|yes|yes|3.36|

1/ This set includes English ComrDn Law, Genmn Conunercial Law, Scandinavian Comrercial<sup>Law.</sup> 2/ This set includes Latin Armrica, Asia, Africa and Middle East, Developed Countries.

3/ This set includes unemployment rate, fiscal deficit, openness, Rrowth, and sie of public sector.

_Note:_ Table 9 reports the group of instruments used in the first-step regression. The rows give the names of the<sup>dependentvariables in thefirst-step</sup> regression. The columns describe the different groups of instruments used in each regression writing a yes if that group<sup>is usedin the estimationof each</sup> dependent variable. The last column gives the F-statistic on the excluded instruments.

32
AJLYTICAL IBY OF THIRTY ET STWIES C INDEPDET VMIAMES GRTH RATE --------------------------------------------------------------NARACECALCILATIW RIMUIAL SECTO (it VMIAR ES RUCT ItICS OF PERIOD MAD 0 D EMIIICITIUS SOCIORP 6E TYPE AM SPE ANLYSIS VNWUUTAL POLICY PU1IETIOI FOLITICAL OF SIUY S3U WETRIES PERIMD V RUIAE VAlARLES OirN FCTS VSAlES R _ES NA_ Show the GDP Dinlcd wd 11 yrm furicAturl Inwestnt rate 1st period (1W53) **detaulrmt** role (World drwti'w 10 was pkdxction + R-q **u 0.46** of qrfc. _raft_ u) coitrie Averae _anal rwth rat_ PopAlaticn guith in listrial gram rte + rato ad period gra ad 1st period 56 1960-70 E rt grwth R-9u **_*_** 0.61 ca"_ tly the 2d period: 81 1970-79 rat ntire aon= + To ealiate the DCs Inflation rate strauth of the 1at period: 41 -stinte, the and period: a8 residal of the furtian of the grwt rate estinted without the agricultural ewiabt* wa thisasm verible . .... ...... .... .......... ... ............ .. . ..................................... .. ...... .. KAIUSI Stu4 Vh w 73 mket19 wrs Rate of Irtsnt grcth R-sqr - 0.66 (1W" ) ralettiiihip (Word cmc DCs Averg rat growth of **rat at** corotat total Ile bet n agpot BWt) dividd into 1960-78 godo sct price growth am tw mapes: at cnt + The iqet proiet gor - 37 lowprices Poptabtion growth stidid is acoordir to - 36 md-i + rate dciefly that deve1opat levet Ditto, + carted by of the DC. ad The oltweighted by Wgrwth of the mat ee of uspotliw Irv reof eap art an their cquntr cmtries ad _*maifactwilrg_ factor those with 1960 Iq2orts in prodctivity pqpAru below total uqots 1 fillion for the _coustrias_ **amle** for the modiu_-ir countries ................ .............................. ....... .......... ............ ...................... ..................................................................
The savings and capital formation of the financial sector is comparativoly small: its role is as an intermediary and it therefore plays a bigger part in Table 2. The foreiln sector is a residual: its net lending will equal the sum of that of remaining sectors. Naturally, the gross saving and capital formation of the rest of the world is not recorded in the national statistics of any one country; only the rest of the world's dealings with the country in question are included.

Table 2, the capital finance account, shows the financial assets and liabilities which were accumulated in the year by each sector. In principle these should sum to the net lending total for the sector shown in Table 1, but in practice there is a discrepancy resulting from the deficiencies of the different sources of data on which the two tables are based. While Table 1 is drawn primarily from information in the income outlay accounts, Table 2 is typically based on balance sheets and asset market transactions.

Consulting the columns for the household sector, the accumulation of financial assets corresponding to the net lending position of this sector is evident from the assets column. The sector acquired bank deposits alone in an amount equivalent to 4.5% of GNP. Its accumulation of claims on the funds of life assurance companies and pension schemes was only somewhat smaller, and there was considerable acquisition of long term securities and stocks, and other equity claims. But what is noteworthy is that the net surplus of the sector conceals considerable heterogeneity of behavior. Thus the household sector also borrowed appreciable sums from financial institutions as can be seen from the liabilities column. There was also an accumulation of indebtedness in trade credit. It is thus important to bear in mind that even a single entity may at the same time accumulate financial assets and liabilities, and this applies a fortiori to a large sector. The 8
- 19 -

As shown in Cohen and Michel (1988) calculating a time-consistent policy simply amounts to finding a feed-back decision rule which, here, can be writtent ( 19 ) Pt , bQt in which b is the largest amount that the lenders can ask at time t #hen it is expected that future payments will be set according to another rule: (20) Pt+ b~ Qt.s which they take as given. A time consistent strategy is one for which, at the equilibrium, b - b.

The equilibrium is calculated in Appendix 3. It is shown that the equilibrium growth rate is nothing else but the post-defauLt path and that b X ) In other words the "time-consistent" policy is simply one in which the lenders take every period the costs that the borrower would incur by defaulting and, as a result, their rescheduling strategy simply mimics the post-default path that the country could follow on its own.

As apparent from equation (19) a time consistent rescheduling strategy act as a tax on outputs the borrower expects that the lenders will ask for as much as it can pay and this is an amount which, it can foresee, will be proportional to how much output it can generate. These expectations increase the shadow cost of capital in the debtor country and reduce investment immediately, making it optimal for the lenders to do what they are expected to: disregard the incentive to invest and ask for as much as they can.
LLC to avoid taxes. The incentive to register firms for redundant or non-business activities might be greater in developed countries with more complex (and enforced) tax and regulatory structures.

# **_2.4 Data and Summary Statistics_**

The sample for the analysis is a pooled, cross-sectional, longitudinal unbalanced panel of 90 observations across 40 countries with non-missing explanatory variables in both the GEM and WBES databases for 2003, 2004 and 2005.<sup>10</sup> Summary statistics are shown in Table 1. The mean spread with nascent entrepreneurs (SPR_N_C) is -0.36% and the spread with young firms (SPR_B_C) is -1.55%, which suggests that on average the two measures are very similar. However, we find a standard deviation of over 4% for both indicators – maximum values of over 9% and minimum values less than -9% – and variation across economic and political environments.

We consider a variety of country characteristics as predictors of entrepreneurial activity, which vary over time. We include log GDP per capita (GDPPC) in all estimations, to control for economic development because of the varied levels of development of countries for which we have data. As an additional explanatory variable, we include the ratio of domestic credit to the private sector as a percentage of GDP as a measure of financial development (DomCredit).

We use four measures of the regulatory barriers: First, an indicator of the difficulty of hiring and firing employees (Labor_Rig). Second, the log cost of business registration (Entry_Cost). Third, the log number of procedures required to start a business (Entry_Proc). Fourth, the ease of closing a business, proxied by the estimated recovery rate claimants can expect following foreclosure or bankruptcy (Rec_Rate). These measures indicate the difficulties in starting, operating, and closing a business.

It is important to note that these indicators measure the barriers for a “typical” <u>formal sector</u> firm, which might in part explain the weak relationship with GEM data. For instance, the methodology for entry barriers assumes: “The business is:

- A limited liability company.

> 10 The complete list of countries is shown in Annex B.

8
- 9 -

Heckscher-Ohlin trade model. 1/ Carliner (1985), Noland (1987), and Lawrence (1987) fall in this category.

12. The precise objects of econometric investigations of these studies were varied. Some studies attempted to determine whether the level of total imports in Japan was unusually low, relative to comparator countries, while some other studies were primarily concerned with the level of manufactured imports of Japan. Yet, some others investigated both of these issues. Furthermore, some studies compared Japan's performance with other industrial countries, while some others compared Japan with mixed groups of industrial and developing countries.

13. Table 4 summarizes the key characteristics and conclusions of the studies reviewed below. The order of review is generally based on the chronology of publication, but partly modified by the typology explained above.

II. Saxonhouse (1982, 1983, 1985, 1986) 14. Saxonhouse (1982) estimated a Heckscher-Ohlin (H-O) type model of trade involving 109 product categories, for a sample of 9 countries 1/ The growing empirical relevance of intra-industry trade among the industrial countries was first pointed out by Bela Balassa (1966), who hypothesized that economies of scale, imperfect competition and product differentiation were the basis for the growth of such trade. Based on the Dixit-Stiglitz (1977) analysis of monopolistic competition, Krugman (1979, 1980, 1982) and Dixit and Norman (1980) developed rigorous theoretical models to explain the basis for intra-industry trade. Lancaster (1980) and Helpman (1981) also developed a similar but somewhat different approach. For a convenient review of the development of the economics of intra-industry trade, see Greenaway and Milner (1986). As a short-hand expression for these theories, we will refer to the Helpman-Krugman (H-K) model below.
# **Appendix Tables and Figures**

Table A1: Distribution of sample by previous treatment assignment

||Surveymode experiment|
|---|---|
||Phone survey<br>WhatsApp|
|Treatment|150<br>149|
|Control|150<br>150|
|P-value|0.928|

_Notes:_ This table presents the distribution of the survey experiment sample drawn from the sample of the main impact evaluation being analysed in Amaral et al. (2021). Within each treatment arm, we randomly selected a group of 300 individuals (for a total of 600) and randomly assigned them to one of the survey-mode treatments. “ _P_ -value” indicate the _p-_ value of the test for differences in treatment status (Treatment or Control) in the main impact evaluation by assignment to any survey-mode (Phone-based survey or WhatsApp).

Table A2: Construction of the Comparable Nationally Representative Sample

||Percentage|Observations|
|---|---|---|
||100%|75045|
|Eligibility by age of children|51%|38,097|
|Eligibility by age (own)|47%|17,733|
|Eligibility of main caregiver|98%|17,315|
|Eligibility of access to mobile phone|91%|15,680|
|Eligibility Sample|21%|15,680|

_Notes:_ This table presents the total sample from the 2019 Household Survey (EHPM) that meet the eligibility criteria for the impact evaluation. _Eligibility by Age of Children_ indicates if the individual lives together with at least one child 8 years old or younger. _Eligibility by age_ is met if the individual’s age is between 18-65 years. _Eligibility of main caregiver_ indicates if the person is either a parent or a grandparent of a child within the household. Finally, _Eligibility by access to mobile phone_ was created using the information on whether the person has a mobile phone. Columns (1) and (2) show the percent and number of individuals in the EHPM sample that meets each of the consecutive eligibility criteria. For example, 17,733 individuals (47 percent) are adults between 18-65 years of age living together with at least one child 8 years old or younger.

37
women, representing a 12% increase from the control baseline of 35%. For men, the increase is 4.7 percentage points, or a 13.5% rise over the control baseline. Reassuringly, we do not observe any differences in timely payments between treatment and control for pre-intervention bills.

Our study reveals that the impact of the personalized tax letters extended beyond the targeted October 2020 bill. The bottom right panel of Figure 8 provides compelling visual evidence that the letters encouraged some neighbors to clear past-due debt from previous billing periods, particularly following the implementation of COVID-19 lockdown measures in Argentina in April 2020. Despite receiving the letters in early October, the intervention also prompted some taxpayers to pay their November and December bills on time. Crucially, the magnitude of the responses was similar for both men and women.

We further exploit the detailed granularity of our property tax data to examine the payment dynamics of the October 2020 bill by gender, both before and after the due date. To this end, we first rectangularize the dataset using the payment date variable to obtain a daily balanced panel of payments. For each individual _i_ , the payment indicator _pay_ billid_ is set to zero up until the day the payment is made, and equal to one thereafter. We then run the following fully saturated regression where _γd_ are day indicators spanning September 25th to October 31st, _Treatid_ is an indicator for those who received a letter, interacted with the day dummies, and _Xid_ are controls. We run this regression separately for women and men and plot the coefficients together in Figure 9. Intuitively, _βd_ captures the cumulative difference in payment rates between the treated and untreated neighbors for each day _d_ , providing insight into the dynamic effects of the intervention.

The top panel in Figure 9 presents the distribution of payment dates for the October 2020 bill among women and men, with most payments occurring before the due date on October 9th. The bottom panel reveals an immediate and statistically significant increase in the payment rate among treated units. Notably, men are more sensitive and respond earlier to the tax letter than women. The compliance effect for men emerges both numerically and statistically earlier than women, reaching a magnitude of about 5 percentage points. While men’s treatment effect remains relatively constant after the due date (October 9th), treated women continue to make overdue payments, gradually narrowing the gender compliance gap.<sup>10</sup> Lastly, we investigate heterogeneous responses across property value quintiles and by gender,

> 10. Figure C.8 in the appendix presents the analog of Figure 9 for the pre-treatment July 2020 bill. Reassuringly, the evidence indicates a zero pre-treatment effect on payment rates between treated and untreated groups for either men or women.

13