rafmacalaba/datause-encoder-data huggingface dataset:
Red Sea, and on the east to the western shores of the Indian subcontinent. Each dot on the map in Figure 2 represents a reported incident attributed to Somali pirates.

Out of these 1,099 attacks, 216 were successful, resulting in the hijacking of ships and crews (these instances are depicted by a red dot in Figure 2). Figure 3 plots Somali attacks and the success rate, i.e. the fraction of attacks that were successful in a given year. The success rate spiked in the early years of piracy to drop equally sharply in 2009, at the same time that international navies started to get deployed to police the Indian Ocean, and again in 2011 as private armed security contractors were increasingly placed on board of vessels sailing through pirate-infested waters.<sup>2</sup>

**Somali pirates’ modus operandi** At the origin of a pirate attack is the initiative of an instigator who would assemble a team consisting of an assault crew and a “hold-out” team in charge of logistics once a vessel is brought to shore for ransom negotiations. An assault is conducted out of two to three skiffs with between 10 to 20 persons on board. Pirates would be armed with be with and weapons, equipped navigation equipment, dispose of a ladder to board vessels (World Bank 2013). The targeting of vessels is premeditated rather than the outcome of a random encounter at sea; pirates are all equipped with marine radars, which identify the characteristics (size and type) of vessels with Automatic Identification Systems (AIS) — a mandatory equipment for all large commercial vessels (300 GRT and above) and all passenger ships (International Maritime Organization 2016) and widely adopted otherwise for anti-collision and search-and-rescue purposes.

If a vessel is successfully hijacked, she is brought back to Somali waters and anchored off the coast. A “hold-out team”, which comprises a negotiator and a posse dedicated to securing access to the beach to deliver protection, food, water, and energy to the hijacked ship and her crew, then takes over. The negotiation team’s connections with local power brokers and the of the local therefore a critical role on stability political landscape play the pirates’ ability to sustain protracted negotiations with the shipping companies’ representatives (World Bank 2013).

Once an agreement is reached, the ransom is paid and the vessel released. We estimate

> 2Since 2015, two attacks have been reported but none resulted in a ransom payment.

7
The secondary sector also picked up, from 2.9% per annum to 4.5%.<sup>39</sup> So too did the primary sector, but at a much lower level; the pre-1991 growth rate in primary NDP per capita was only 0.2% per annum, while it rose to 0.8% in the post-1991 period.<sup>40</sup> As one would expect, the share of the primary sector in NDP has fallen appreciably from 55% in the early 1950s to 15% in 2012.

The acceleration in survey-based mean real consumption growth—from 0.6% per annum in the pre-1991 period to 2.0% in the post-1991 period—is significant. The urban and rural trends reveal a pattern that is similar to the national level, though there are some differences between the two segments of the economy (Table 2). The NSS mean consumption growth rates are appreciably higher (nearly twice as high) post-1991 in both rural and urban areas.

Faster growth post-1991 has also come with rising inequality within urban areas, but less so in rural areas. Figure 2 plots the indices over time. Note that these are the “raw” values, without adjustment for the effect of MRP rounds. When we add controls for MRP rounds, we also see rising inequality in rural areas post-1991. Table 2 gives the exponential trends for the Gini index. In contrast to a declining trend in rural Gini and no trend in urban Gini in the pre1991 period, a significant positive trend in inequality emerged post-1991 in both sectors. The rise in inequality has been greater in urban areas. At the same time, inter-sectoral inequality has been generally rising. We see from Figure 3 that the ratio of urban mean to the rural mean has been rising since around 1970 (the early observations were volatile, and should probably be discounted), though with signs of levelling off and even decline since 2000. Controlling for MRP rounds, time trends in the ratio of real urban-to-rural mean consumption are similar pre- and post-1991, with the ratio increasing at about 0.14% per annum in both periods. So the higher growth in the post-1991 period has come with generally rising consumption inequality, especially within sectors, but also to some extent between them.

Datt and Ravallion (2011) also found higher trend rates of poverty reduction in the post1991 period, but the differences (compared to the pre-1991 period) were only statistically significant for the headcount index (nationally and for urban areas). Our new series provides a more statistically robust indication of acceleration in progress against poverty post-1991 for both sets of poverty lines (Table 1). Over the 55-year period as a whole, the exponential trend decline (for the higher line) was 1.3% per annum for the headcount index, rising to 2.3% and 3.0% for

> 39 The standard errors are 0.1% and 0.3% respectively.

> 40 The standard errors are 0.05% and 0.2% respectively.

12
The assumption needed to guarantee that this identification strategy is valid is that, in the absence of Banco Azteca opening, the average difference between outcome variables across municipalities with and without Azteca would have been the same post-October 2002 as pre-October 2002. That is, outcome variables can differ in levels across municipalities with and without Azteca, but they cannot differ in changes.

This assumption would be violated if, for example, the number of informal businesses were on a steeper growth path in municipalities with Azteca than in municipalities without Azteca. If this were the case, we could detect a positive effect of Banco Azteca opening on the number of informal businesses, when there was actually no effect.

While the identification assumption is fundamentally untestable since we do not observe the counterfactual of Banco Azteca not having opened, we verify whether the assumption is likely to hold in three different ways. First, as is common in the literature, we test for parallel pre-event trends in outcome variables across municipalities with and without Azteca. If the changes were the same in the pre-period, they are likely to have remained the same in the post-period in the absence of Azteca opening.

Second, we run regressions that control for the possibility of municipalities with and without Azteca having different linear time trends. If our estimated effects are driven entirely by differences in trends, then these effects should disappear once we control for time trends in the regressions. We do this in two ways: First, we allow all municipalities with Azteca branch to have the same time trend, and all municipalities without Azteca branch to have a different time trend. Second, we allow each municipality to have its own time-trend (i.e. municipality-specific time-trends).

Finally, we examine graphically whether the estimated change in outcome variables in municipalities with Banco Azteca coincides with the time of Banco Azteca opening.

# **III. Data**

All of our main outcome variables come from the Mexican National Employment Survey (ENE). The ENE is the survey that the Mexican government relies on for calculating unemployment statistics and for measuring the size of the informal sector. It has been conducted quarterly since 2000-II and covers a random sample of approximately 150,000 households each quarter. Households remain in the survey for five consecutive quarters. We 8
|**10 Relief for education of school students**|**3,528.4**|**_5,300.5_**|**3,318.1**|**4,947.4**|**5,400.8**|
|---|---|---|---|---|---|
|Tax bracket I|6,123.2|4,634.6|1,726.7|2,955.6|2,271.6|
|Tax bracket 11|2,690.4|11,455.2|3,686.7|8,230.1|4,705.8|
|Tax bracket III|2,610.8|..|5,418.6|14,398.2|11,080.6|
|11**Travel of**children to school**outside place of**<br>**residence**|..|48.5|52.4|50.0|63.4|
|Tax bracket I|..|46.8|52.1|50.3|63.7|
|Tax bracket 11|..|58.4|51.3|41.8|58.5|
|Tax bracket III|..|139.8|102.0|62.5|54.6|
|12 Education**of children in non-public schools**|119.0|225.4|145.8|212.0|230.3|
|Tax bracket I|61.8|164.5|98.4|179.5|194.5|
|Tax bracket 11|129.8|271.5|226.5|374.9|313.7|
|Tax bracket III|337.4|482.2|511.3|415.9|451.7|
|13 Paid health performances|..|..|.|62.8|68.3|
|Tax bracket I|..|..|..|59.2|63.7|
|Tax bracket 11|..|..|..|74.7|80.1|
|Tax bracketIII|..|..|..|88.0|112.8|
|14 Supplementary education and supplementary<br>vocational training of taxpayer|37.5|96.2|107.2|104.7|114.5|
|Tax bracket l|34.7|89.4|103.1|106.2|111.9|
|Tax bracket 1I|47.0|126.7|112.5|98.6|127.3|
|Tax bracket III|60.7|158.3|227.5|84.4|139.2|
|**15 Education of taxpayer in higher-education schools**|..|..|..|224.5|278.1|
|Tax bracket|..|..|.I|219.9|273.7|
|Tax bracketI|..||..|279.1|319.5|
|Tax bracket III|..|_|..|297.1|320.8|
|16.1 Small housing relief - for house or dwelling|132.1|147.9|210.6|244.8|246.9|
|renovation of residential||||||
|Tax bracket I|107.9|124.6|170.7|227.4|234.2|
|Tax bracket II|179.2|214.4|318.9|363.9|328.3|
|Tax bracket III|328.8|406.0|933.8|534.2|514.9|
|16.2 Large housing relief- for residential house or|1,036.1|995.0|1,378.9|975.0|1,464.1|
|dwelling construction<br>of||||||
|Tax bracket I|390.7|482.3|553.2|644.2|796.8|
|Tax bracket II|1,053.2|1,337.9|2,064.9|2,048.4|2,596.0|
|Tax bracket III|<br>6,169.4|<br>6,578.7|<br>10,569.9|3,698.1|6.645.5|
|**17 Stocks (individual capital**gain?)|**318.5**|**363.0**|**571.1**|||
|Tax bracketI|230.0|283.2|484.5|..||
|Tax bracket II|326.3|386.7|642.3|..||
|bracket III|572.8|687.3|1,053.5|..||
|Source: The Ministry of Finance of Poland||||||

18
for influencing legislation, or with measures of the impacts on business of bribing to affect Parliamentary votes or government decrees.

Correlations of NIT, ICRG, CPIA and EIU with the various WEF firm survey corruption variables show a broadly similar pattern. Each of those four is strongly correlated with bribes for utility connections, exports and imports, and tax collection. Their correlations with a WEF measure of favoritism in decision making are more modest. There is one state capture measure in WEF, however, which is strongly correlated with the four broad indicators: the WEF question on “business costs of corruption,” defined in terms of other firms’ illegal payments to influence government laws and policies. With this single exception, data from the two firm surveys indicate that NIT, ICRG, CPIA and EIU are measuring administrative corruption much better than the measure state capture.

A striking finding from the BEEPS and WEF data is the absence of any significant link between corruption in public procurement and the broad, perceptionbased measures. Of the 12 correlations between three firm-survey variables on bribery in procurement on the one hand, and the four broad indicators on the other, the highest correlation is .27. The third-strongest of these 12 correlations (between CPIA and a BEEPS measure) even has a perverse sign.

This weak relationship could be attributable in part to lack of good information, if most firms never sell their products or services to government agencies. Accordingly, we re-calculated the two BEEPS items on corruption in public procurement, deleting the roughly four-fifths of firms in the sample reporting no sales to their government.

38
|**Tabl**|**e A2: Summ**|**ary statistics of**|**the main variab**|**les**||
|---|---|---|---|---|---|
|**Variable**|**Mean**|**Standard**<br>**deviation**|**Minimum**|**Maximum**|**Observations**|
|R&D Activity|0.179|0.383|0|1|12,980|
|Informal Competition (Country-<br>level cell average)|0.533|0.209|0.002|0.985|12,980|
|Firm Size|3.255|0.600|2.398|4.595|12,980|
|HHI|0.112|0.167|0|1|12,980|
|GDP per capita (lagged, logs)|8.682|1.036|6.493|10.333|12,980|
|Age of Firm (logs)|2.724|0.659|0|5.242|12,980|
|Manager Experience (logs)|2.767|0.676|0|4.094|12,980|
|Exports (proportion of sales)|0.108|0.250|0|1|12,980|
|Female Top Manger Y:1 N:0|0.137|0.344|0|1|12,980|
|Multi-firm Y:1 N:0|0.184|0.388|0|1|12,980|
|Power Outages (day hours)|1.422|4.409|0|30|12,980|
|Finance Obstacle (0-4 scale)|1.524|1.330|0|4|12,980|
|Overdraft Y:1 N:0|0.440|0.496|0|1|12,980|
|Courts Obstacle (Country-level<br>cell average)|0.966|0.639|0|2.987|12,980|
|Population (lagged, logs)|16.261|1.510|12.526|20.928|12,980|
|Inflation (%, annual)|6.774|7.546|-1.988|53.229|12,980|
|Gross Tertiary Enrollment Rate|0.287|0.239|0.005|0.855|12,980|
|Gross PrimaryEnrollment Rate|1.004|0.168|0.249|1.462|12,980|

63
hand in hand with a decline in the competitiveness of the US economy, in particular in the manufacturing sector.

# **I. The euro and financial integration and deregulation in Europe**

6. The road to financial integration in Europe began in early 1957, when under the so-called European directives most countries began to lift capital controls and deregulate interest rates, with a view to creating a single market in banking and financial services. However, only by the late 1980s and early 1990s did most countries embark definitively on a financial liberalization strategy, spurred by the single European Act of 1987 (Table 1).

||**Table 1: Financial li**<br>|**beralization and inte**<br>|**gration in Europe **<br>||
|---|---|---|---|---|
||**Lifting of capital**<br>**controls**|**Interest rate**<br>**deregulation**|**First Banking**<br>**Directive**|**Second Banking**<br>**Directive**|
|**Belgium**|1991|1990|1993|1994|
|**Denmark**|1982|1988|1980|1991|
|**France**|1990|1990|1980|1992|
|**Germany**|1967|1981|1978|1992|
|**Greece**|1994|1993|1981|1992|
|**Ireland**|1985|1993|1989|1992|
|**Italy**|1983|1990|1985|1992|
|**Luxembourg**|1990|1990|1981|1993|
|**Netherlands**|1980|1981|1978|1992|
|**Portugal**|1992|1992|1992|1992|
|**Spain**|1992|1992|1987|1994|
|**United Kingdom**|1979|1979|1979|1993|
|**Source: Buch and Hei**|**nrich(2002).**||||

7. In 1999, the adoption of a five-year financial harmonization program—the Financial Services Action plan (FSAP) —brought about a new level of financial integration. The FSAP‘s main objectives were (i) the development of a single market for wholesale financial services; (ii) the creation of open and secure retail markets; (iii) the establishment of clear, efficient, prudential rules and supervision of financial services; and (iv) the establishment of the conditions for an optimal single financial market. The Lisbon agenda (2000) and the re-launching of the Lisbon strategy (2005) further reinforced the goal of creating a single financial market. These reforms resulted in a fully integrated financial market. Using the Chinn-Ito index which measures openness in capital account transactions the table below shows that countries reached the status of full liberalization after the adoption of the euro in 2002 (Table 2).

4
- efficiency.  They are also, in most cases, statistically significant at the 5% level;

- Privatisation (defined as at least 50% divestment) is consistently associated with higher tele-density.  Interacting privatisation with regulation gives a large and highly significant interaction term; the coefficient on the regulatory index remains positive and significant but is reduced in size by about 40% while the coefficient on ownership falls and is no longer significant.

- Competition (defined as competition either in fixed or in mobile services) has no general significant effect either on its own or when interacted with the regulatory index and, even when significant, has a relatively small coefficient estimate;

- Allowing for the endogeneity of competition, privatisation and regulation make little difference to the coefficient estimates; fixed effect models dominate random effects models; dynamic models out-perform static models but the estimates are dominated by large lagged dependent variable coefficients and introduce additional estimation issues.

- For a smaller sample of 12 lower income Latin American and Caribbean countries, the estimated regulatory coefficients are, more often than not, significant at the 5% level but rather smaller than for the 22 country sample. Conversely, the estimated effects of privatisation and competition are stronger than in the full sample.  Gutierrez points out that, although this may reflect potentially greater difficulties in establishing effective regulatory agencies in low income countries, it may also reflect the fact that the regulatory agencies are, on average, younger in the low income sample.

The issue remains as to how far the index based on formal legal properties is a good proxy for the total effect of regulation, including variations in regulatory practice. The analysis in Section 2.4.1.3 above suggests that it may be a reasonably good proxy but that analysis is based on a data-set with limited – and perhaps (in hindsight) - over-optimistic data on the performance of regulatory agencies. Hence, the question remains open.

# **5.4 Conclusions from Econometric Modelling of the Impact of Regulation on Utility Industry Outcomes**

The key conclusions from this section are:

- 1) _There is some evidence from recent econometric studies that the existence of an independent regulatory agency increases investment and productivity levels.  The evidence is strongest for telecoms._

The recent telecom studies have had access to better data than the electricity studies. Even though none of the telecom studies have had information on regulatory practice, they have produced evidence of a positive effect either directly and/or by augmenting the effects of competition and privatisation. The strong positive results from the use of an <u>index</u> of regulatory characteristics in Gutierrez (2003) is particularly noteworthy. Thus far, for 43
18 Table 6: Relative Import Prices for High and Low Madagascar<sup>Tariff Products:</sup> Based on Statistics Reported by Industrial<sup>Countries</sup> (Price Relatives Using Industrial Country Comparisons,

||Low Tariff|Products|High Tariff|Products|
|---|---|---|---|---|
|Exporter|1979-82|1984-88|1979-82|1984-88|
|France|71.3|49.0|66.4|25.9|
|Germany, Italy and Japan|199.6|75.1|185.1|54.8|
|All Abov~e|125.0|70.2|98.9|44.6|

Note: For all of the above comparisons except<sup>France, over the 1979-82 period</sup> the low tariff product average price relative<sup>is significantly</sup> higher (95 percent confidence level) than the high tariff product average. Also, the 1984-88 price relatives for both the high and low tariff products are significantly lower than the 1979-82 figures. The reader should note these results were achieved with a smaller and less representative sample than the findings reported in Table 3.

The data in the table support the proposition that collaborative overinvoicing contributes to product differences<sup>in price relatives. 19 In both 1979-</sup> 82 and 1984-88 low tariff products had significantly higher prices (the 1979-82 pre-PSI difference for French exports was not significant)<sup>which is the pattern</sup> expected under collaborative false invoicing. Moreover, the spread between the high and low tariff products' price relatives<sup>(about 26-percentage</sup> points) is 19It should be noted that the results are<sup>also consisten</sup> with other possible explanations. For example, high tariff items are concentrated in laborintensive sectors where Madagascar has, or could<sup>develop, a productive capacity.</sup> As such, "potential" competition from domestic<sup>producers may moderate foreigners,</sup> export prices, as would be the case if "limit"<sup>pricing were being practiced (see</sup> Yeats, 1976 for a discussion of limit pricing models). Also, the (simpler) labor-intensive, high tariff products may be more "familiar"<sup>to customs agents</sup> so the potential for inflating their prices<sup>is reduced.</sup>
Table 7. Indonesian Exports, ICT use, and Aggregate Labor Market Indicators. All workers. 2005-2010

|Dependent variable:|La|bor force pa|rticipation rat|e||Employ|ment rate|||Unemploy|ment rate||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.0216||0.0228|0.0216|0.00283||0.00334|-0.0111|0.0236||0.0245|0.0507|
||(0.0226)||(0.0223)|(0.0403)|(0.0235)||(0.0233)|(0.0398)|(0.0168)||(0.0169)|(0.0282)*|
|Share of hhlds with computer||0.184|0.182|0.180||0.0778|0.0776|0.0469||0.124|0.123|0.179|
|||(0.0900)**|(0.0901)**|(0.113)||(0.0865)|(0.0866)|(0.106)||(0.0507)**|(0.0511)**|(0.0603)***|
|IDN exports per worker *||||0.00916||||0.109||||-0.197|
|Share of hhlds with computer||||(0.181)||||(0.177)||||(0.120)*|
|Observations|195|195|195|195|195|195|195|195|195|195|195|195|
|R-squared|0.941|0.942|0.942|0.942|0.962|0.962|0.962|0.962|0.934|0.935|0.936|0.936|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010, 2015-2016 Philippines STEP survey, and 2011-2015, and UN Comtrade database 2005-2010.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province controls include the age structure of the population, share of urban population, real GDP per capita, minimum wage level, gender and educational composition of employment. All models control for province and year fixed effects. Robust standard errors between parentheses clustered by province. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

28
- Regime 2 (Export Restriction) – As in Regime 1, but with Tanzania imposing an export restriction on maize, preventing maize exports from rising above their 2001 levels.

As noted above, Tanzania introduced grains export bans in response to the 2007-2008 food price crisis. Tanzania had also relaxed import restrictions on grains as a response to the food price crisis, thereby raising domestic prices; however, these policies are not considered here.<sup>13</sup> Implementation of the export restriction is treated via a complementary slackness condition, with the 2001 benchmark year’s export level taken as the quota level of exports. When changes in economic conditions push Tanzania’s maize exports to increase, they are prevented from rising above the 2001 benchmark year’s level by means of an endogenous export tax instrument. Maize trade flows, however, are permitted to fall below 2001 benchmark year levels if economic conditions dictate a decline in maize exports.

Since producers may adjust their production behavior in anticipation of future export restrictions we carry out one additional set of steps prior to conducting our scenario simulations. For example, if producers anticipate a regime of export restrictions where prices may be low in years with high yield because they cannot export any excess supply, they may choose to move some resources out of production of maize, reducing its benchmark supply. This behavior is approximated by conducting stochastic simulations of historical production variability in Tanzania, under the baseline trade regime and under the export restricted trade regime, to estimate how maize production may adjust on average in these two regimes. Based on the average production adjustment, we estimate factor-tax equivalents that provide an equivalent production shift to provide new benchmark databases for use with Regime 1 and Regime 2.

Finally, it should be noted that maize production perturbations under all case-study scenarios are based on the same 2001 benchmark year, adjusted for pre-shock producer expectations about trade regimes. They thus allow for comparison of the marginal effects of the international climate heterogeneity in the case-study year and of the trade regime in place without introducing additional uncertainty by replicating the global economy in the case study years.

> 13 Several studies have examined the effects of import restrictions for other Africa countries (e.g., Dorosh et al., 2007 and Haggblade et al., 2009).

14