rafmacalaba/datause-encoder-data huggingface dataset:
We combine these infrastructure data with our baseline data and estimate the following specification: _Yict = φ·_ 1 ( _Submar inect ×Connectedi_ ) _+γ·_ 1 ( _Submar inect ×Connectedi ×In f ra_ ) _+β·Zi ×T +_ Γ _i +ηct +μict_ (2) where _In f ra_ is a placeholder for the (high) electrification (road) dummy. _Zi_ is a placeholder for baseline measures such as distance to the coast interacted with time trends. All else remains as previously defined. The coefficient of interest, _γ_ , measures the effect of having a high-speed Internet connection via the submarine cables in districts with high electrification (road) density. Based on the results in Tables 1 and 3, we do this analysis for total FDI and FDI in services.

Results are presented in Tables 4 and 5. Starting with Table 4, columns 1-9 show the interaction effects on FDI in all sectors. Across the various specifications, we find positive and statistically significant effects on the interaction between high-speed internet connectivity and the high electrification dummy.<sup>26</sup> Interestingly, the effect of Internet connectivity in places with low electrification is not statistically significant. This provides suggestive evidence of the complementary role that access to electricity plays in amplifying the effects of Internet connectivity. The interaction between road density and internet connectivity is largely (statistically) insignificant, albeit positive.

Next, we focus on FDI in the services sector. Once again the interaction with electrification is highly significant across all specifications. In column 10 for instance, the results show that high-speed Internet connectivity in districts with high electrification footprints increases the probability of attracting FDI into services by 8.1 pp relative to Internet connectivity in districts with low electrification. The interaction with road density is also positive and statistically significant in all cases except columns 12, 15, and 18 where we estimate the joint interaction effects together in the same specification. Nonetheless, the results here suggest that access to road networks also amplifies the effects of Internet connectivity in attracting FDI into the services sector.

Further, in Table 5, we examine the role of access to electricity and roads in amplifying the effects of Internet connectivity on FDI into finance and technology services as well as the other (retail and health) services categories. Again, access to electricity plays a crucial role in enhancing the effects of Internet connectivity on FDI in these service subsectors. For instance, Internet connectivity increases the probability of attracting FDI to finance, technology, and health and retail services by 5.3 pp (column 1), 6.9 pp (column 10), and 4.3 pp (column 19) respectively

> 26The effects are insignificant in 2 out of 6 cases.

16
Panels B-D in Table 1 and Figure 2.A-C disaggregate the enterprise employment numbers by ownership and by economic sector.<sup>12</sup> Panel B of Table 1 and Figure 2.A show that foreign firm employment is mainly concentrated in the manufacturing sector, accounting for about 93.2 percent of total foreign employment in 2004.<sup>13</sup> In particular, Figure 2.A highlights the predominance of labor-intensive manufacturing in foreign employment and its rapid growth from 0.24 million in 2000 to 1.1 million in 2007. As foreign-owned enterprises are more likely to be engaged in international trade than domestic enterprises,<sup>14</sup> the concentration of employment generated by them in labor-intensive manufacturing may reflect Vietnam’s comparative advantage in labor-intensive manufacturing. Further disaggregation reveals that a large percentage of Vietnam’s foreign sector employment was concentrated in the clothing and footwear industries (47.1 percent in 2004), and that these industries have an exceptionally high proportion of female workers (84.8 percent in 2004). The high female-intensity in these industries undoubtedly skews the overall gender distribution for foreign employment in Vietnam.

Perhaps reflecting the growing importance of assembly activities by foreign firms in electronics and other high-tech industries (Athukorala and Tien, 2012), employment generated by foreign firms in the skill-intensive sector rose rapidly albeit from its small initial level, more than quadrupling from 0.054 million in 2000 to 0.25 million in 2007. As employment by foreign firms in the skill-intensive sector expanded at faster rates than corresponding employment by

> 12 The manufacturing sector is further disaggregated into labor-, capital-, and skill-intensive sectors. The laborintensive sector includes textiles, apparel, footwear/leather products, wood products, furniture/miscellaneous manufacturing, metal products, and non-metallic mineral products. The capital-intensive sector is defined as comprising the mining, food products, basic manufacturing, and chemical, rubber, plastic products industries. The skill-intensive sector consists of electronics/machinery and transport equipment. See footnote 24 for the corresponding Vietnam Standards Industrial Classifications (VSICs).

> 13 The statistics for the year 2004 is reported since it is the year of my empirical analyses.

> 14 In their responses to the annual _Enterprise Survey_ , 73.1 percent of foreign enterprises in manufacturing indicated that they had some direct exports in 2004 whereas the corresponding figures are 28.1 percent and 11.7 percent for state and for private enterprises respectively. 76.5 percent, 26.2 percent and 9.3 percent of foreign, state and private enterprises, respectively, had some direct imports in 2004.

7
Before the intervention, I collected self-reported data on personal and family characteristics from enrolled students. Follow-up self-reported data included questions to measure the intervention’s impact on attitudes, violence, and crime as well as enrolled children’s exposure to risky spaces. I combined this self-reported information with neurophysiological evidence, particularly measures of stress and emotional regulation, from a random subsample of enrolled students. I used low-cost, portable electroencephalograms within an in-field lab setting. Finally, I also collected administrative records on grades, behavioral reports, and absenteeism data from the students. Schools provided these data before and after the intervention.

I report three sets of results. First, when I compare both types of group composition, integration is better than segregation. Results show that, on average, improvements in attitudes and misbehavior at school are larger when participants are in more diverse groups than when they are segregated. This is true for both high- and low-violence children. These results align with the evidence that interactions with diverse peers can generate differences in the learning experience (Lafortune et al., 2016).<sup>7</sup> In this sense, students in heterogeneous groups have the opportunity for exposure to both good behaviors they should follow and negative ones they should reject. These interactions are less available for students in the homogeneous group. In terms of neurophysiological outcomes, I find that students exposed to similar peers in terms of violence—that is, assigned to homogeneous groups—have stress levels greater than those of students placed in a heterogeneous setting. The increase in stress is particularly great for children treated in a homogeneous and highly violent group versus comparable children treated in heterogeneous groups.

Second, I studied tracking effects on marginal students, who are just above or below the median of the propensity distribution function within each stratum. By virtue of my design, very similar students around this cutoff were assigned either to homogeneous highly or less violent groups. By

> 7Alternatively, these results support the rainbow model of peer effects, whereby all individuals benefit from exposure to a more heterogeneous peer set (Hoxby, 2000).

5
explained by sector nor size characteristics. Again, this finding points to significant underinvestment and hoarding of earnings by Top Tier firms.

# **Exhibit 2.16 – Statistical differences between the median Top Tier firm and the median Second Tier firm**

_<u>Quantile regressions for period 2003-2007</u>_

||_Difference_<br>**Median d**|_Statistically_<br>_different?_<br>**ifference, (pp)**|_Difference_<br>_Statistically_<br>_different?_<br>**Median difference also taking**<br>**firm size into account, (pp)**|
|---|---|---|---|
|**Net fixed asset growth, %**|1.1|No|0.2<br>No|
|**Retained Earnings to Equity, %**|15.7|Yes|15.5<br>Yes|

_Source: Coface Egypt; World Bank staff analysis Note: Differences are expressed in percentage points. Statistically different refers to a p-value less than 0.1._

- _2.2.3 Leverage: Deleveraging is taking place in all firms, although Top Tier firms have higher total debt levels; Second Tier firms have similar total debt as firms in the LMI group, but are more reliant on shorter maturities_

Top Tier firms have both higher total debt and total liabilities levels than the Second Tier (14.7 pp and 24.4 pp, respectively), as Exhibit 2.16 shows. Second Tier firms also clearly have less access to long term finance as short term debt as a portion of total liabilities is 6.3 pp higher. The disproportionately higher level of current liabilities of Second Tier firms further suggests that Second Tier firms rely much more on trade credit, potentially a result from worse access to finance conditions. This suggestion is further supported by the fact that paid-in capital is much higher for the Second Tier (9.7 pp).

An international comparison is possible, since Coface and Worldscope leverage data are very close. Exhibit 2.17 shows that the Second Tier is leveraged in line with top firms in LMI countries, as evidenced by only a slightly higher debt level (0.5 pp). Total liabilities levels are with 63.5 percent both higher than the peer group and the LMI group. In combination with higher current liabilities and short term debt, this confirms that Second Tier firms rely relatively more on short term maturities and trade finance. The differences in profitability clearly reflect that the interest coverage ratio is higher in Egypt, despite debt levels also being higher.

As for industry differences, Top Tier firms have higher total debt levels in all sectors, except for Consumer Staples (Exhibit 2.18). In addition, all sectors have been showing strong deleveraging over the period 2003-07, with the exception of the Top Tier in Industrial Cyclical (CAGR 23.2 percent). This finding is further supported by the strong growth in interest coverage across the board. In addition, we observe an overall increase in current liabilities for all tiers. However, for all Second Tier firms, short term debt decreased, except for Construction and Materials, showing the increasing importance of different credit sources like trade finance. In contrast, short term debt increased for Top Tier firms, with the exception of Consumer Staples.

15
<!-- Start of picture text --> Income distribution in LAC<br>2003-2014<br>2<br>“ 7 7 » 2014<br>zB 2003 rg 4 ' ‘ a<br><!-- End of picture text --> <!-- Start of picture text --> /An<br>N “ \S<br>re ‘Y<br>ovo —<br>co Vulnerable39% Middle35% Class ~~ >=»<br>oO =< ~—s<br>$4 $10 $50<br>Per capita income USS (2005 PPP)<br><!-- End of picture text -->
||F|ull Sampl|e|Tri|mming Tail|5%|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Power Structure_|||||||
|YRD|0.273|0.262||0.301**|0.287**||
||(0.216)|(0.218)||(0.135)|(0.137)||
|Guerrilla||0.162|||0.130||
|||(0.128)|||(0.113)||
|YRD×GuerrillaNo [_b_0]|||0.209|||0.220|
||||(0.248)|||(0.142)|
|YRD×GuerrillaYes [_b_1]|||0.375*|||0.380**|
||||(0.201)|||(0.152)|
|_Initial Conditions_|||||||
|Ln_GVOPC_52|-0.025|-0.014|-0.014|-0.055|-0.045|-0.041|
||(0.128)|(0.125)|(0.124)|(0.116)|(0.113)|(0.111)|
|Ln_pop_52|-0.254|-0.301|-0.293|-0.073|-0.113|-0.119|
||(0.201)|(0.203)|(0.200)|(0.118)|(0.123)|(0.122)|
|_Geography_|||||||
|Share of Plain (%)|0.011|0.011|0.011|-0.000|0.000|-0.001|
||(0.011)|(0.011)|(0.011)|(0.008)|(0.008)|(0.008)|
|Distance to Xiamen|0.001|0.001|0.001|-0.000|-0.000|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Obs.|55|55|55|51|51|51|
|_R_<sup>2</sup>|0.087|0.095|0.095|0.084|0.105|0.110|
|_H_0 : _b_0 = _b_1|||0.309|||0.221|

Table 17: The Effect of Factions and Guerrilla Presence on the Changes in the Average Years of Schooling for Individuals Born in 1952-1972 Relative to Those Born Prior to 1930, as Observed in the 1990 Census. Note: The dependent variable is the difference in log of the average education level of the cohort born between 1952 and 1970 and the log of the average education of the cohort born before 1930, as observed in the 1990 Census. In the right panel, the 5% tail observations are trimmed from the analysis. The last row reports the p-values of the hypothesis that _b_ 0 = _b_ 1. White standard errors are in parenthesis.

44
This general improvement in living standards is corroborated by trends in ownership of private assets. The three rounds of the Nepal Living Standard Surveys asked for ownership of 15 private assets, plus ownership of computers introduced in 2003. Figure 3 summarizes asset ownership of these 16 items by three groups based on per capita consumption: bottom 40 percent, middle 40 percent and top 20 percent. Different items exhibit different paths of improvements in ownership and some items were owned by almost all households in 1995 (e.g., kitchen utensils) and others are rarely owned even in 2010 (e.g., cars and washing machines). It appears in general that a sharp increase in ownership is first experienced by the top 20 percent between 1995 and 2003, followed by the middle 40 percent and then the bottom 40 percent in later years. In 1995, few households in the bottom 40 percent and the middle 40 percent owned items such as phones, TV/VCR/VCD and fans but their ownership increased drastically by the middle 40 percent by 2010. On the other hand, ownership of more expensive items, such as motorcycle/scooter, refrigerator/freezer and computer improved but only exclusively among the top 20 percent.

The improvements in living standards along monetary measures of welfare have been accompanied by dramatic improvements in non-income dimensions of well-being. Figure 4 shows progress on reducing deprivation along measures of well-being typically used in the construction of a Multidimensional Poverty Index (MPI).<sup>15</sup> Significant improvements have been made in dimensions such as education, health and access to basic services. For example, households with school-aged children (6 to 15 years old) out of school declined from 55 percent in 1995 to 16 percent in 2010. This is presumably due to improvements in accessibility to and availability of school, as households with children spending more than 20 minutes to reach primary school declined from 84 percent to 33 percent during the same period. But much of it is also likely due to increased ability to afford schools, including private schools. In 1995, almost one in every two households that experienced a childbirth did so without the presence of a medical professional. This declined to one in five by 2010. Approximately four out of five households did not have an in-home toilet in 1995. By 2010, two out of three households had one. The non-income dimensions of well-being have also improved among the poor, although the progress was not as pronounced as improvements for the overall population (see Figure 5). In particular, improvements in access to sanitation and access to reasonable cooking fuels have been much less dramatic among the poor than those for the population as a whole.

> 15 There are several estimates of Multidimensional Poverty available in Nepal from various data sources. For example, see OPHI (2013) and Mitra (2014).

9
Table A2: Robustness Checks

||(1)<br>Baseline|(2)<br>Cases|(3)<br>Adjusted Pop|(4)<br>Scale by<br>Susceptible Pop|(5)<br>Restrict<br>Phone Data|
|---|---|---|---|---|---|
|Imported Incidence/Cases|1.094***|1.077***|1.070***|1.091***|1.495**|
||(0.357)|(0.191)|(0.376)|(0.349)|(0.601)|
|Lag Incidence/Lag Cases|0.563***|0.684***|0.619***|0.625***|0.575***|
||(0.129)|(0.157)|(0.0716)|(0.0867)|(0.128)|
|Rain in cm|-0.000654|-0.273|0.000636|-0.000471|-0.00140|
||(0.00790)|(0.191)|(0.00822)|(0.00811)|(0.00792)|
|Lag Rain in cm|0.0269|0.188|0.0243|0.0268|0.0262|
||(0.0181)|(0.639)|(0.0178)|(0.0183)|(0.0181)|
|Lag 2 Rain in cm|0.0319**|0.860*|0.0322**|0.0306**|0.0315**|
||(0.0156)|(0.507)|(0.0148)|(0.0150)|(0.0152)|
|Constant|-0.0688**|-1.579|-0.415|-0.142|-0.0719**|
||(0.0290)|(1.199)|(0.264)|(0.164)|(0.0298)|
|Month FE|Yes|Yes|Yes|Yes|Yes|
|Health Post x Month Obs|432|396|432|432|432|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Notes: Augmented version of the Arellano-Bond Generalized Method of Moments estimator used in all specications. In Column 2, I use cases rather than cases per 1000 people. In Column 3, the population of the health post catchment area is adjusted monthly by the number of people entering and exiting based on the mobile phone data scaled by a factor of .7 to account for the population not represented by these data. In Column 4, lag incidence is scaled by the percent of population susceptible to malaria. In Column 5, movements are restricted to those where the SIM card had a call or a text within 14 days of when the movement is counted.
|_Ih_w_<br>_h_<br>_Cmounuy_|_i kaW A_<br>_Gb_<br>_Comea_<br>_Uniei_<br>_& Locadoe_|_Year_|_Language_|_Acad_<br>_staff_|_Sudaed_|
|---|---|---|---|---|---|
|Lebanan|LUbn<br>Academy of<br>Fine Arts, Beirut|1937||||
||Higain<br>Coilege, Beirut|1955|E|20 F.T.<br>30 P.T.|36D|
||Beirut Universty<br>College, Beirut|1950|E, A|c 60 F.T.<br>130 P.T.|1,700|
|Llby|U. of Garyounis,<br>Ben8gazi|1955/<br>1974|A, E|c. 510|4,720+<br>4,040 et|
||U. of Al-Faleh,<br>Tripoli|1957/<br>1974|A, E|c 830|c. 7,500|
||Sebha U., Sebba|1983|A|84-85<br>12D|845<br>Z,XO)|
|Morocco|HaI n U Univ.,<br>Cablunca|1975|||c. 21,000|
||Mhammed I Univ.,<br>Oujda|1979||c350|c. 7,000|

**a** MOE-Mlity **of** Educadon; MOHESR-Mlisty of Higher Education and Scientific Racb

b Ag-AIclture; Ar-Achiecur A-Artr; CS-Compuer Sc4 D-Dentisty; Ed-Educadoe; E-Engineering Ft=Food Techn4 **La Lguage** L-Law, **MMedckine; P-Phamy,; R-Religous** Studies; S-Sciences; T-Ted _ologr, Vm-Veterinaiy Med.

_44_
AFPs. Because of the lack of a central computer and clearing system, account switching costs are not insignificant. Although often overlooked, account switching also imposes heavy costs on employers, who are required to withhold contributions from payroll and pay them over to individual AFPs according to the choice of their workers. High marketing costs also bedevil the life annuity market. The Chilean authorities are considering various measures to reduce operating costs. These include restrictions on the frequency of the right to transfer accounts and restrictions on the nature and size of marketing costs. Some consideration is also given to the regulation of the structure of commissions paid to agents, especially in the life annuity market.

Table 3

**Operating** Costs and Investment Returns

||Singapore<br>**1990**|Malaysia<br>**1989**|Zambia<br>**1988/9**|Chile<br>**1990**|
|---|---|---|---|---|
|Operating Costs as % of|||||
|Annual Contributions<br>Average Total Assets<br>Covered Annual Wages|0.53<br>0.10<br>0.21|1.99<br>0.18<br>0.40|51.7<br>6.8<br>5.17|15.4<br>2.3<br>1.54|
|Real Investment Returns<br>on Individual Accounts<br>(during 1980s)|2.86|4.82|_-55.0*_|7.5-10.5|

guesstimate based on nominal rate of interest of 5% and average inflation rate of _135%._ The data on investment returns, which are calculated after the deduction oZ operating costs, show that Chilean workers did better on balance than either Malaysian or Singaporean workers, although investment returns in these two countries also benefitted from the high real interest rates that prevailed in many countries during the 1980s. However, the concern in Chile arises from the realization that the very high investment returns of the 1980s are unlikely to be

**18**
as a major destination for Costa Rica’s exports, from 26% in 1982 to 38% in 1992 and 46.5% in 2003 (Figure 1.2). This was in part the result of a significant increase in foreign investment inflows, with the establishment of companies such as Componentes Intel in 1998.

<!-- Start of picture text --> 53.16%<br>20.76%<br><!-- End of picture text --> <!-- Start of picture text --> 26.08%<br><!-- End of picture text --> <!-- Start of picture text --> 47.86%<br>13.91%<br><!-- End of picture text --> <!-- Start of picture text --> 38.23%<br><!-- End of picture text --> <!-- Start of picture text --> 42.12%<br>11.42%<br><!-- End of picture text --> <!-- Start of picture text --> 11.42%<br>46.46%<br><!-- End of picture text --> Source: Prepared by the authors based on figures from PROCOMER.

The good export performance and the higher foreign direct investment (FDI) inflows, together with an adequate foreign exchange policy, have resulted in a relatively satisfactory improvements in the balance of payments’ trade account, since although at present the current account deficit is higher in absolute terms, this deficit has tended to decrease relative to the size of the economy (Figure 1.3).

<!-- Start of picture text --> Figure 1.3 Costa Rica: Current Account Deficit in US Dollars and as a Percentage of GDP<br> 1983-2003<br>0 -1.0%<br>-100 -2.0%<br>-200 -3.0%<br>-300<br>-4.0%<br>-400<br>-5.0%<br>-500<br>-6.0%<br>-600<br>-7.0%<br>-700<br>-800 -8.0%<br>-900 -9.0%<br>-1000 -10.0%<br>1983 1985 1987 1989 1991 1993 1995 1997 1999 2001<br>Current Account Deficit CA/GDP<br>Export Promotion FTZ Law Reform Intel’s Arrival<br>Import Liberalization<br>US$ millions<br>Capital Account Liberalization<br><!-- End of picture text --> Source: Prepared by the authors based on figures from Banco Central de Costa Rica.

The latter is a very important result, in view of the fact that the opponents of the liberalization process forecast that the trade balance would be wrecked as a consequence of import liberalization. Thus, the performance of Costa Rica’s economy during the last two decades demonstrates the profound economic understanding of the designers of the reform, as they took into account the general and not merely the partial equilibrium effects when designing and implementing the process of economic liberalization. In fact, 7