rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2.  Adoption of deposit insurance around the world by region as of 2003***

(Years of establishment/revision in parentheses and number of countries by group in brackets, as of 2003)

**Panel A: Explicit Deposit Insurance**

# **Asia & Pacific [10]**

|Bangladesh (1984)|Malaysia (1998)|Philippines (1963)|Vietnam (2000)|
|---|---|---|---|
|India (1961)|Marshall Islands (1975)|Sri Lanka (1987)||
|Indonesia(1998)|Micronesia(1963)|Thailand(1997)||
|**Europe & Central Asia [20]**||||
|Albania (2002)|Czech Rep. (1994)|Macedonia (1996/2000/2002)|Turkey (1983/2000)|
|Belarus|Estonia (1998)|Poland (1995)|Turkmenistan (2000)|
|(1996/1998/2000/2001/2004)|Hungary (1993)|Romania (1996)|Ukraine (1998)|
|Bosnia-Herzegovina (1998)|Kazakhstan (1999/2003)|Russia (2003)||
|Bulgaria (1996/1998/2001/2002)|Latvia (1998)|Serbia and Montenegro (2001)||
|Croatia(1997)|Lithuania(1996)|Slovak Republic(1996/2001)||
|**Latin America & Caribbean [18]**<br>||||
|Argentina (1979/1995)|Dominican Republic (1962)|Jamaica (1998)|Trinidad & Tobago (1986)|
|Bolivia (2001)|Ecuador (1998)|Mexico (1986/1990/1999)|Uruguay (2002)|
|Brazil (1995/2002)|El Salvador (1999)|Nicaragua (2001)|Venezuela (1985/2001)|
|Chile (1986)|Guatemala (1999)|Paraguay (2003)||
|Colombia(1985)|Honduras(1999)|Peru(1991)||
|**Middle East & North Africa [4]**||||
|Algeria(1997)|Jordan(2000)|Lebanon(1967)|Oman(1995)|
|**Sub-Saharan Africa [5]**||||
|Kenya (1988)|Tanzania (1994)|Zimbabwe (2003)||
|Nigeria(1988/1989)|Uganda(1994)|||

**Panel B: Implicit deposit insurance**

|**Asia & Paci**|**fic [16]**|||||
|---|---|---|---|---|---|
|Afghanistan|China|Laos|Myanmar|Papua New Guinea|W. Samoa|
|Bhutan|Fiji|Maldives|Nepal|Solomon Is.||
|Cambodia|Kiribati|Mongolia|Pakistan|Vanuatu||
|**Europe & C**|**entral Asia [7]**|||||
|Armenia|Georgia|Moldova|Uzbekistan|||
|Azerbaijan|Kyrgyz Republic|Tajikistan||||
|**Latin Amer**|**ica & Caribbean [9]**|||||
|Belize|Cuba|Guyana|Panama|Suriname||
|Costa Rica|Grenada|Haiti|St. Lucia|||
|**Middle East**|**& North Africa [10]**|||||
|Djibouti|Iran|Libya|Saudi Arabia|Tunisia||
|Egypt|Iraq|Morocco|Syria|Yemen||
|**Sub-Sahara**|**n Africa [41]**|||||
|Angola|Central African Rep.|Gabon|Madagascar|Niger|South Africa|
|Benin|Chad|Gambia|Malawi|Republic of Congo|Sudan|
|Botswana|Comoro Is.|Ghana|Mali|Rwanda|Swaziland|
|Burkina Fas|o Cote d'Ivoire|Guinea|Mauritania|Senegal|Togo|
|Burundi|Equatorial Guinea|Guinea-Bissau|Mauritius|Seychelles|Zaire|
|Cameroon|Eritrea|Lesotho|Mozambique|Sierra Leone|Zambia|
|Cape Verde|Ethiopia|Liberia|Namibia|Somalia||
|*Excludes h|igh income countries.|||||

22
labour markets and Government expenditure are considered politically contentious areas<sup>9</sup> rather than as a consequence of welfare maximization criteria.

In the absence of general guidelines, the economists' perspective on reform implementation has taken quite a narrow view and the discussion, especially stimulated by the Southern Cone policy experiments, dealt with few issues such as the precedence of stabilization over liberalization and the order of liberalization (current versus capital account, domestic versus external financial liberalization).

Some statements on the principal steps to undertake have been formulated and are sometimes considered 'common knowledge". They can be summarized as follows. First, stabilization is a precondition to any form of liberalization, because not only macroeconomic instability lessens the efficacy of liberalization , but also trade and financial liberalization have adverse macroeconomic implications. Second, domestic capital market liberalization is a precondition to external capital market liberalization, because otherwise foreign capital would flow out, thus destabilizing an economy with poorly working financial markets.

Third, external goods market liberalization should precede external capital market liberalization, because otherwise major foreign capital inflows would cause the real exchange rate to appreciate, at variance with the requirementa for a successful trade liberalization.

Nevertheless, a broader consideration of the implications of stabilization and liberalization packages raises some question marks even on the few consolidated points reached so far. Special emphasis in the paper is devoted to analyze the role of people's misperception regarding the reforms.

9 Labour markets are not even mentioned among the possible areas of reform in the otherwise comprehensive survey of the main current issues in Latin America provided by Williamson (1990, ch.3).

7
Figure 4. Lorenz curve, Malawi Fourth Integrated Household Survey (IHS4): comparing overall inequality across household per capita consumption expenditure, household per capita wealth, and individual wealth <u>Note: based on the full sample in the IHS4 (26,079 adults).</u> Table 10. Comparison of inequality measures, for individual-level wealth, across the Malawi IHPS 2016 (LSMS+) and Malawi IHS4 2016/17

||Among fu|ll sample:|||Among only<br>|those owni<br>|ng any asset<br>|s (value>0)<br>|:<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||Gini in<br>|dex<br>|Gini<br>|index<br> <br>|75/2<br>|5<br>|GE(0): T<br>|heil L<br>|GE(1):<br>|Theil T<br> <br>|
||IHPS<br>(LSMS+)|IHS4|IHPS<br>(LSMS+)|<br>IHS4|IHPS<br>(LSMS+)|IHS4|IHPS<br>(LSMS+)|IHS4|IHPS<br>(LSMS+)|<br>IHS4|
|_For IHPS:_<sup>_(1)_</sup> Missing values|94.3***|88.3***|92.0***|79.2***|9.0***|5.4***|2.7***|1.4***|3.6***|<br>2.9***|
|imputed through multiple<br>imputation; self-reported<br>values for joint owners|[1.3]|[2.3]|[1.6]|<br>[4.2]|[0.48]|[0.14]|[0.23]|[0.20]|[0.35]|<br>[0.77]|

# <u>Notes:</u>

(1) For IHS4, no imputation versus the multiple imputation model yielded the same inequality estimates (Only 5 percent of land parcels had missing valuations in the IHS4). The specifications varying how joint owners’ valuations are also not relevant in the IHS4, since only one person reported for others’ ownership and valuation of each land parcel (hence one valuation per parcel).

(2) Bootstrapped standard errors (250 repetitions) are in brackets. Similar to Table 7, all differences between HH per capita and individual wealth inequality estimates were statistically significant at p<0.001 (and, likewise, from the inequality indicators based on household per capita consumption). Differences between the IHPS and IHS4 estimates are statistically significant at p<0.001.

37
-6-

No one knows the exact infant mortality<sup>rate for Mali as a whole.</sup> The following table reproduces<sup>the figures found in recent World Bank</sup> publications: Mali: Infant Mortality Ra<sup>e</sup> for Certain Years Between 1960 and<sup>1966</sup>

|Refe|rence|1960|1965|1982|1983|1984<br>1985|1986|
|---|---|---|---|---|---|---|---|
|(141|p. 17|210||||||
|(15]|p. 180|179||134||||
|(lC1|p. 250||207|||176||
|(161<br>|p. 92-<br>||184<br>||148|||
|t11|1<br>p.288||200|||174||
|[71|p. 320||207||||144|

The variations shown in this<sup>table would merely demonstrate the</sup> fragility of the data were it<sup>not that the weakest estimates are given in the</sup> publications that deal specifically<sup>with demographics</sup> and population policy. For its part, a document prepared<sup>under the aegis of WHO confines itself to</sup> citing, without the slightest comment,<sup>rates (furnished</sup> by surveys of visibly uneven quality) ranging from<sup>118 to 255 per thousand (cf. (17 pp. 28-29). The</sup> national documents refer to the<sup>assessments made by the international</sup> organizations or unhesitatingly use studies done by amateurs<sup>(for example</sup> [181).

The health services are in a crisis,<sup>as every one knows. A recent</sup> survey [18], while improving on<sup>the previous Ministry of Health estimates by</sup> at least 502, leads to extremely low<sup>utilization rates (cf. [191 pp. 26-31):</sup> for the 4.8 million residents of districts<sup>_(cercles)_</sup> that have no hospital, just over 700,000 episodes have been<sup>counted (but are thes -oisodes in the</sup> true sense?), which gives a utilization<sup>rate of 152. For ,,u 2.8 million</sup> people living in _cercleo_ that possess a hospital, just over<sup>1.5 million</sup> episodes were counted, giving a rate<sup>of 55S. The hospitalization</sup> rate ia at most 1.4X.

It should be noted that the statistics<sup>recording staff are in</sup> pracice unable to distinguish between contacts<sup>(any contact between a sick</sup> person and a health professional)<sup>and episodes (or new patients). When some</sup> written trace of the activity remains,<sup>only the former are entered in the</sup> records and totaled in the statistical<sup>statements under the heading of</sup> episodes (whereas they obviously<sup>relate to contacts); and when the number of</sup> contacts has to be supplied, the number<sup>of "episodes" is multiplied by an</sup> arbitrary figure, often close to<sup>3. As a result, utilization rates are always</sup> greatly overestimated. It follows from<sup>the above figures that certainly less</sup> than one Malian in three today has<sup>contact with the health services during the</sup> course of a year; the rate was higher<sup>than 1 out of 2 in 1975 (cf. [201 p.</sup> 284; see also, for 1974, [21] p. 43).
Figure 7: Marginal - Information (Sep ’13 survey).

Figure 8: Marginal - Delivery (Sep ’13 survey).

<!-- Start of picture text --> 0 1<br>Information<br>.8<br>.6<br>Probability<br>.4<br>.2<br><!-- End of picture text --> <!-- Start of picture text --> 0 1<br>Recipient<br>.8<br>.6<br>Probability<br>.4<br>.2<br><!-- End of picture text --> Figure 9: Marginal - Partisanship (Sep ’13 survey).

<!-- Start of picture text --> 0 1<br>Partisanship<br>.8<br>.6<br>Probability<br>.4<br>.2<br><!-- End of picture text -->

# _5.1. Robustness checks_

In order to further validate the main results, this section proposes a number of robustness checks considering different samples, alternative definitions for the variables of interest and additional controls. Given the 26
<!-- Start of picture text --> 1995 2004<br>.7 Any remittances .7<br>Foreign remittances<br>.6 .6<br>.5 .5<br>.4 .4<br>Mean<br>.3 .3<br>Mean<br>.2 .2<br>.1 .1<br>0 0<br>1 2 3 4 5 6 7 8 9 10 11 12+ 1 2 3 4 5 6 7 8 9 10 11 12+<br>1995 2004<br>60 60<br>50 50<br>35 35<br>20 20<br>5 5<br>0 0<br>-5 -5<br>1 2 3 4 5 6 7 8 9 10 11 12+ 1 2 3 4 5 6 7 8 9 10 11 12+<br>Household size Household size<br>Proportion of household receiving remittances<br>Total amount of received remittances, if recieved, 000s<br><!-- End of picture text --> Figure 1: Incidence of migration and amount of remittances by the household size. Whiskers indicate 95% confidence intervals for the means. Histogram of the household size on the background of the lower two panels. NLSS 1995 and 2004 39
rule should obviously be started in good years, which in turn has the added benefit that expenditure volatility will not only be brought down to the level of income volatility, but will be reduced further.

To test the VE hypothesis versus the DO hypothesis, we run a regression of government spending (also its components – current and capital spending) on oil-related fiscal revenues over the period 1970 – 2005. All data are presented in constant dollars. We first show the results for regressions with symmetric adjustment in years with rising and years with falling revenues: see Table 1 below.

The regression shows a very strong relation between expenditure and revenues from oil, as is to be expected. The coefficient indicating the dependence of aggregate expenditure on fiscal revenues from oil is significantly higher than one. Moreover there seems to be a strong bias toward current expenditure, the relevant coefficient exceeds the coefficient in the investment only regression by more than two-thirds.

But there is also a serial correlation pattern in the errors. The DW coefficient is too low in all three regressions, and the Breusch-Godfrey Serial Correlation LM test rejects the H0 hypothesis of no-serial correlation. Analysis of the error terms furthermore suggests a before and after 1984 difference. The Chow Breakpoint test for 1984 strongly rejects the H0 of no structural break in 1984 in all three equations. See Annex 2 for the results of these tests.

**Table 1 Government Spending and Fiscal Oil Revenues – Nigeria (1970-2005, levels in constant $)**

||Total Government Expenditure|Current Expenditure|Capital Expenditure|
|---|---|---|---|
||1.45|0.87|0.52|
|Oil Revenue|(9.87)|(10.29)|(6.36)|
||1.76|1.10|0.26|
|Constant|(0.83)|(0.90)|(0.22)|
|No. of Obs.|36|36|36|
|R-sq. overall|0.73|0.75|0.53|
|DW|1.13|1.46|0.73|

Note: 1. Data source: IMF World Economic Outlook database. 2. t-statistics in parentheses.

We included a dummy term for the period up to 1984 and now also include asymmetric adjustment coefficients: a dummy that equals zero in years of rising revenue and one in years of falling revenue is entered multiplicatively with the oil revenue term. One should expect a negative coefficient under the Voracity hypothesis, and a positive coefficient under the DO hypothesis. Table 2 lists the results.

19
reduce government revenues but may also undermine the trade regulatory framework of the importing country, and ultimately alter the competitive environment by resulting in an unfair advantage gained by the fraudsters over their law-abiding competitors. By avoiding NTMs, trade fraud may jeopardize public health or safety, and weaken environmental protections. The three examples cited above in italics highlight such fraud, whereby traders either misreport country of origin or product codes to evade NTMs. We focus on the NTMs applied at the border, hereafter referred to as Border NTMs.<sup>9</sup> Examples of Border NTMs include customs controls, quota licensing, pre-shipment inspections, and additional fees paid at customs, among many others. Some of these NTMs are bilateral in nature, targeting speci...c products from speci...c partner countries. Compliance with these requirements is often ...nancially costly and/or time-consuming,<sup>10</sup> therefore providing incentives for traders to commit trade fraud by misrepresenting the country of origin or misclassifying product codes on their customs documents (Li and Lin, 2022).

To study the role of Border NTMs in causing trade fraud, this paper ...rst provides a simple theoretical model built on Swenson (2001) and Ferrentino, Liu and Wang (2012) to show that the restrictive NTMs cause importers and exporters to under-report the quantity traded on their respective customs declaration forms. However, given that the penalty and detection rates of the importing countries are generally higher than those of the exporting countries, importers will under-report less than exporters hence giving rise to the observed trade discrepancies. The theoretical model also shows that trade discrepancies increase with the ad valorem equivalent (AVE) of NTMs. Furthermore, the model shows that the e¤ect of AVE on trade discrepancies is larger when products are homogeneous and when the exporting countries are developing countries. In addition, the model rationalizes origin and

> the import value or volume and the export value or volume is more than 40 percent, in order to side step variations in the valuations and small statistical errors.

> 9For more details see Ederington and Ruta (2016) and UNCTAD (2015).

> 10See UNCTAD, World Bank (2018).

3
The categorization is not perfect, but this is the best we could do given that VHLSSs do not provide a breakdown to finer sectors. For example, one could argue that “vehicle sales” could serve manufacturing production and hence could be a part of the production channel. In addition, hotels could serve visitors outside a province and hence not reflect consumption of households residing in that province. The latter case is possible but not quantitatively important, because restaurant employment overwhelms hotel employment. Restaurant and hotel employment accounted for 1.65 percent and 0.12 percent of the workforce respectively, according to the Population and Housing Census of 1999. Nevertheless, the breakdown provides useful information about which channels took place.

Table 6.1 provides the results for the three channels. Let’s first focus on the demand channel, which is reflected in columns 1, 2, 3 and 4. Both panels yield a consistent result that a larger provincial tariff reduction causes significantly higher employment in sales of vehicles and vehicle parts (column 1). A 10% tariff reduction causes employment in motor vehicle sales to rise by 0.58 percentage point. Tariff reduction is negatively and statistically significant for employment in wholesales at 1 percent level for both Panels A and B. A 10% decrease in tariff increases employment in wholesales by 2 percentage points (column 2 of Panel A). Surprisingly, employment in local retail increases in both panels with large magnitude, but the coefficients are not statistically significant (column 3), a point to which we will return to discuss in detail. In addition, tariff reductions do not have any impact on employment in hotels and restaurants (column 4).

We do find strong evidence for the “production spillover” channel. Change in household employment in supporting industries is highly statistically significant at the 1 percent level for both measures (column 5 of Panels A and B). Note that “supporting industries **”** refers to other 27
population threshold (as measured in the most recent population census).<sup>14</sup> The reason for this is that transfers were given out universally in treatment villages, and so our partner NGO chose smaller villages to be able to preserve their liquidity.

In Liberia, we implemented the project in two waves: a smaller first wave (90 villages), which had its endline in late 2020; and a bigger second wave (210 villages), which had its endline in September-November 2021. Most of our ACASI protocols were developed, tested, and refined over the course of the Wave 1 endline. Therefore, this sample is excluded from our results, and our results for Liberia are restricted to Wave 2 only. In Malawi, all 300 villages were enrolled at once and the endline was in April-July 2021.<sup>15</sup> In both countries, we attempted to enroll 10 households per village into data collection for program evaluation, though in some cases we were only able to enroll fewer households.<sup>16</sup> Surveys were targeted at female heads of households, i.e., either the spouse or partner of the male head for dual-headed households or the solo head for female-headed households<sup>17</sup> (male heads of dual-headed households were interviewed only when the female was not present, and could not be reached within a few days of the baseline study. When the male head was interviewed, the IPV module was not asked).

# **2.2 Questionnaire Design and ACASI Experiment**

## **Measuring Intimate Partner Violence**

To measure IPV, we employed WHO’s standard Violence Against Women module.<sup>18</sup> The questionnaire includes 20 questions about experience with specific forms of violence, over a time period of 12 months prior to the survey. Following the literature, we group these questions into four

> 14In Malawi, the upper threshold was 100 households per village; in Liberia, it was 125, reflecting the larger village sizes in the study region.

> 15Figure A1 presents the project timeline.

> 16The total sample size for the cash transfer study is 2,715 in Liberia and 2,944 in Malawi. Yet we conducted the IPV module at endline only to women who currently have a partner or have been so in the past 12 months prior to the survey date. For the IPV measurement experiment, we further excluded those who reported to have vision or hearing impairment. We are also excluding the Liberia Wave 1 sample from analysis in this paper. As a result, the total sample size for this paper is 1,737 women in Malawi and 1,231 in Liberia.

> 17We find that solo-headed households are rare. As we show in Table 1, 97% of the sample in both countries had a partner at the time of our study.

> 18The WHO’s standard questionnaire for measuring IPV, which is widely used for measuring IPV, can be found here: https://www.who.int/gender/violence/who ~~m~~ ulticountry ~~s~~ tudy/Annex3-Annex4.pdf. Our survey module on IPV can be found in the Appendix.

6
# **Table 5: Cross-Country Regressions with Creditor Rights and Legal Efficiency**

The dependent variable is the ratio of the number of bankruptcies to the number of firms. Transition countries are excluded from all regressions because of the unavailability of disaggregated creditor rights. The regressions are estimated using ordinary least squares with robust standard errors. _LN(GDPPC)_ is the log of GDP Per Capita. _GGDP_ is the 1-year growth rate of real GDP. _D_SYSTCRISIS_ is a dummy equal to 1 in the case of a systemic bank crisis (Caprio and Klingebiel, 2000). _RINTEREST_ is real interest rates. _CREDITOR RIGHTS_ is the sum of dummies identifying Restrictive Reorganizations, No Automatic Stay on<sup>Assets,Secured</sup> Creditors Paid First, and Management Does Not stay in Reorganization (La Porta, et al., 1998) _RULE OF LAW_ is an assessment of the "efficiency and integrity" of the legal environment as measure by Business Intemational Corp (La Porta, et al. 1998 and Pistor, 2000.) t-statistics are in parentheses, *, *, and *** indicate significance at the 10%, 5%, and 1% respectively.

||(1)|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
||**Creditor**<br>**Rights**|**Restrictive**<br>**Reorganization**|**NoAutomatic**<br>**Asets**|**Secured**<br>**PaidFirst**|**Management**<br>**DoesNotStay**|
|_Constant_|-0.3620<br>(-0.44)|-0.4936<br>(-0.59)|0.0931<br>(-0.11)|-0.2632<br>(-0.32)|-0.6489<br>(-0.85)|
|_LN(GDPPC,1-)_|-0.0011<br>(-0.65)|-0.0014<br>(-0.82)|-0.0014<br>(-0.80)|-0.0012<br>(-0.71)|-0.0010<br>(-0.57)|
|_GGDP,,_|-0.1040**<br>(-2.44)|-0.1068***<br>(-2.65)|-0.0993***<br>(-2.56)|-0.1024**<br>(-2.37)|-0.1060***<br>(-2.55)|
|_D_SYSTCRISIS,.,_|0.3072<br>(0.58)|0.3739<br>(0.72)|0.1153<br>(0.22)|0.3444<br>(0.64)|0.2558<br>(0.46)|
|_RINTEREST,_1_<br>_RINTEREST,,_|0.0004<br> <br>(0.03)|-0.0007<br>(-0.04)|-0.0114<br>(-0.77)|-0.0025<br>(-0.16)|0.0074<br>(0.47)|
|_CREDITOR RIGHTS_|(-0.34)1|||||
|_RESTRICTIVE_<br>_REORGANIZATION_||0.5378**<br>(2.12)||||
|_NOA UTOMATICSTA Y_<br>_ONASSETS_|||-0.9093***<br>(4.75)|||
|_SECUREDCREDITOR_<br>_PAIDFIRST_||||-0.2384<br>(-0.77)||
|_MANAGEMENTDOES_<br>_NOTSTAY_|||||0.2073<br>(0.79)|
|_RULEOFLAW_|0.3506***<br>(3.61)|0.3393***<br>(3.76)|0.3715***<br>(3.83)|0.3623***<br>(3.55)|0.3654***<br>(4.28)|
|_YearDummies_|Yes|Yes|Yes|Yes|Yes|
|_Adj.R-Squared_|0.18|0.20|0.23|0.18|0.18|
|_Observations_|252|252|252|252|252|

31