rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 2: Regression**<br>**from the ES on the**|**s of the per**<br>**DB value**<br>**Percenti**<br>|**centiles of the**<br>**les of ES distr**<br>|**firm level distribution ac**<br>**ibution for firm level**<br>|**ross countries**<br>**Value of DB**<br>|
|---|---|---|---|---|
||**re**|**sponse as dep**|**ndent variable**|**days at kink**|
||**10**<sup>**th**</sup>|**25**<sup>**th**</sup>|**75**<sup>**th**</sup><br>**90th**|**(terciles)**|
|||**Imports**|||
|**Std. Dev of ES**<br>**percentile**|1.226|2.399|8.167<br>16.158||
|**Constant**|0.970|1.350|2.469<br>7.261||
|**Slope in first**<br>**tercile of DB**|0.031|0.054|0.487<br>0.806|18.00|
|**Slope in second**<br>**tercile of DB**|0.019|0.088|0.160<br>0.294|32.00|
|**Slope in upper**<br>**tercile of DB**|0.037|0.052|0.119<br>0.231||
|**N**|91.000|91.000|91.000<br>91.000||
|**R2**|0.148|0.173|0.133<br>0.108||
|||**Construction**|**Permit**||
|**Std. Dev of ES**<br>**percentile**|5.873|11.820|43.296<br>106.811||
|**Constant**|2.603|-2.046|11.402<br>-100.589||
|**Slope in first**<br>**tercile of DB**|0.024|0.118|0.354<br>1.685|172.00|
|**Slope in second**<br>**tercile of DB**|0.024|0.013|0.123<br>-0.276|255.00|
|**Slope in upper**<br>**tercile of DB**|-0.002|-0.031|-0.051<br>0.006||
|**N**|73.000|73.000|73.000<br>73.000||
|**R2**|0.040|0.057|0.053<br>0.083||
||5.873|11.820|43.296<br>106.811||
||**Startin**|**g a business/op**|**erating license**||
|**Std. Dev of ES**|||||
|**percentile**|2.541|6.706|18.038<br>44.126||
|**Constant**|4.062|8.642|31.392<br>52.244||
|**Slope in first**<br>**tercile of DB**|-0.042|-0.001|-0.005<br>0.701|22.00|
|**Slope in second**<br>**tercile of DB**|-0.063|-0.228|-0.243<br>-0.273|44.00|
|**Slope in upper**<br>**tercile of DB**|0.022|0.055|0.091<br>0.128||
|**N**|87.000|87.000|87.000<br>87.000||
|**R2**|0.090|0.087|0.020<br>0.010||
|**Notes: **|||||

18
# 3. WHAT **DRIVES CROSS-COUNTRY DIFFERENCES IN INTERNET ACCESS'?**

# **3. 1 A naive look at the facts**

Internet has the potential to provide a wide array of benefits, ranging from education to business opportunities, hence it is crucial to understand how people have access to it. Internet subscriber penetration across Latin America is about 1%. About 98% of these connections in 1999 where through analogue modems. This correlation between telecoms access and internet access is illustrated in Figure 1 for a sample of 20 Latin American countries in 1999. The linear trend line provides quite a good fit.

**Figure 1.** A **naive correlation between telecoms access and internet use**

<!-- Start of picture text --> e9<br>o 3 ~~~~~Y 0 U Zz6x -U 8463<br>- Fe=- 7718-<br>-2-<br>Main lres per 100 INabta<br><!-- End of picture text --> Table 3 summarizes some indicators available for Latin America on the spread of internet access in the region. It is interesting to be reminded that for a large majority of the countries of the region, internet access dates from the second half of the 1990s.<sup>8</sup> T'able 3 shows that the number of internet service providers (ISPs) is generally high enough in most countries to suggest that competition is generally not a major issue in this specific activity. But usage charges continue to be quite high. In 1999, the average cost to be on line conmbining ISP costs and local call charges was US$ 52, twice the equivalent cost in the USA.

**Table 3. Indicators of potential access to the internet**

||Internet<br>access<br>since:|ISPs<br>(1999)|Internet<br>hosts/10000<br>inhabitants<br>(2000)|Internet<br>usersas%<br>ofpop.<br>1999)|Internetchargesin<br>1999(US$ monthly costof20<br>hoursoffpeak)|Personal<br>computers<br>(QOOs)<br>(1999)|CableTV<br>subscribers<br>(OOOs)(1999)|
|---|---|---|---|---|---|---|---|
|Argentina|1989|170|38.48|2.5|27.9|1,800|5890(in1998)|
|Bolivia|Jul.95|9|1.14|0.4|15.07|100|70|

> 8 As a side result of the differences in penetration rates, 1999 business to business transactions in Latin America were estimated at about US$ 288 million, with about 2/3 concentrated in Argentina, Brazil and Mexico. The equivalent figure for the US was US$ 114 billion. See Morgan Stanley Dean Witter (2000), The Latin America Internet Report, February

6
|_sexual behavior_|_exually active)_<br>(5)|0.150|(0.146)|0.241***|(0.028)|3,070|0.149|Yes|-0.0631|3.625|nifcance at the 1, 5|dependent variable||live with the family,|education, muslim,||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_ards_<br>_Risky _|_ehav-_<br>_(for s_||||||||||nd * denote sig|ng values of the||ingle, does not|than secondary||
|_des_<br>_tow_|_sexual b_<br>(4)|0.148|(0.090)|0.292***|(0.017)|4,986|0.099|Yes|0.00186|3.452|***, ** a|ns. Increasi||h spoken, s|ned higher||
|_Attitu_|_risky _<br>_ior_||||||||||clusters).|eplicatio||n, englis|her obtai||
|_HIV testing_|(3)|0.336**|(0.128)|0.449***|(0.016)|4,986|0.261|Yes|-0.139|4.145|g centre level (80|, based on 10,000 r||l, years of educatio|ary education, mot||
|_HIV attitudes_|(2)|0.339***|(0.103)|0.366***|(0.019)|4,986|0.179|Yes|0.0439|3.474|ring at the screenin|s in square brackets|s of Shuga.|tly attending schoo|higher than second||
|_knowledge_|(1)|.781***|(0.214)|.346***|(0.020)|4,986|0.197|Yes|0.0400|6.106|usted for cluste|djusted p-value|th the message|ale, age, curren|father obtained||
|_HIV_||0||0|||||Control)|(Control)|s in parentheses adj|spectively. FWER a|s more consistent wi|gression include: fem|ndex, home owner,|ive language.|
|_Yt):_||||||s|||Var. (|p. Var|rd error|level, re|utcome|each re|wealth i|as a nat|
|_ar. (_||d||||ation|red|ls(_a_)|Dep.|v De|tanda|rcent|d to o|ols in|d size,|oruba|
|_. V_||te||1||erv|ua|tro|n|De|. S|pe|pon|ntr|ol|Y|
|_p_||ea||_−_||s|sq|n|a|d|es|10|s|o|eh|ks|
|_De_||Tr||Y_t_||Ob|R-|Co|Me|St|Not|and|corre|(a) C|hous|spea|

xviii
16 The heterogeneity in food price inflation, both spatial and across commodities, reinforces the importance of conducting the welfare analysis with data that is disaggregated along both dimensions. Like Friedman and Levinsohn (2002) - but unlike most other recent studies - we exploit substantially disaggregated local information on both prices and expenditure shares in the estimations presented below.

# _The Expenditure Effect_

As noted above, we begin by estimating a first-order approximation to the expenditure effect of the price increases on household welfare. This effect is given by the first term on the RHS of equation (6): ( ). It is estimated for each household _h_ using expenditure data on budget shares from the POF, and price increases from the INPC, in the region where _h_ is located.<sup>8</sup> The expectation, of course, is that the distribution of this effect across households will be regressive. Food is a necessity, with a “textbook” Engel curve: the share of expenditure allocated to food declines with total expenditures throughout the entire domain (Figure 4): around 32 percent of expenditure goes into food among the poorest households, compared to about 10 percent at the top of the distribution (captured by POF).<sup>9</sup> This expectation is confirmed by the expenditure effect PIC, which is given by equation (7) and depicts the percentage reduction in welfare at each percentile of the distribution ranked by income per person. The expenditure effect PIC is given separately for Brazil‟s large urban areas in Figure 5, for rural areas in Figure 6, and for the country as a whole in Figure 7.<sup>10</sup> The bands around the PIC are bootstrapped 95% confidence intervals. Horizontal lines denote the average effect (and the confidence interval around it). Average expenditure effects are fairly substantial, ranging from 5 percent of baseline income in large urban areas, to 12.5% in rural areas. For Brazil as a whole, the average reduction in welfare due to the expenditure effect of the 2007-2008 food price increases was of the order of 7.5% of initial income. As expected, the expenditure effects are markedly regressive behind these averages: they range from an average of around 7% (12%) in the poorest decile to 2%

> 8 This is strictly true for the 11 large urban areas where prices are collected directly. As noted in Section 3, households living outside the 11 large urban areas are assigned the vector of price changes from the nearest large urban area, as detailed in Table 2.

> 9 The share is likely to be much lower among Brazil‟s truly rich households but, as is often the case, these are seldom well-captured in household surveys. See Korinek et al. (2006).

> 10 The country as a whole consists of rural areas, large urban areas, as well as all other urban areas, which are not shown separately.
**Table 6. Results: The Impact of Rule of Law and Innovation on the Level of Development**

||**Explanatory Variable**|**Instrument**<br>_Depen_|**OLS**<br>**Coeff.**<br>_dent Varia_|**IV Coeff.**<br>_ble: Log of G_|**Hausman**<br>**Test for**<br>**Endogeneit**<br>**y**<br>_DPper Cap_|**Sig. of**<br>**Residuals**<br>**(rule,**<br>**lpats) in**<br>**Income**<br>_ita in 2000_|_(PPPadju_<br>**Coefficient**<br>**Stage Reg**|_sted)_<br>**of 1st.**<br>**ression**|**Sig.of**<br>**Instrumen**<br>**t in**<br>**Income**|**Shea Test**<br>**for IV**<br>**Relevance**<br>**(adj.**<br>**partial**<br>**R^2)**|**Obs.**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1)|rule<br>oil<br>landlock<br>Sub-Saharan Africa<br>malaria|lmort|0.80 *<br>0.48 *<br>-0.29 **<br>-0.60 *<br>-0.51 *|1.14 *<br>0.57 *<br>-0.14<br>-0.61 *<br>-0.29|Fail to reject<br>Null<br>Hypothesis|No|-0.30 *||No||66|
|2)|rule<br>oil<br>latitude<br>landlock<br>Sub-Saharan Africa<br>malaria|lmort|0.83 *<br>0.35 *<br>-0.01 *<br>-0.34 **<br>-0.51 *<br>-0.74 *|0.95 *<br>0.39 *<br>-0.02<br>-0.27<br>-0.52 *<br>-0.66 *|Fail to reject<br>Null<br>Hypothesis|No|-0.34 *||No||53|
|3)|rule<br>Log(patents per capita)<br>oil<br>latitude<br>landlock<br>Sub-Saharan Africa|lmort<br>lip|0.55 *<br>0.17 *<br>0.36 *<br>-0.01 *<br>-0.36 **<br>-0.38 **|0.45 ******<br>0.24 *<br>0.38 *<br>-0.01 *<br>-0.36<br>-0.33|Fail to reject<br>Null<br>Hypothesis|No<br>No|rule<br>-0.34 *<br>0.06|lpats<br>-0.76 *<br>1.31 *|No<br>No|0.25<br>0.31|53|
|4)|rule<br>oil<br>landlock<br>Sub-Saharan Africa<br>malaria|eurfrac|0.80 *<br>0.44 *<br>-0.29 *<br>-0.60 *<br>-0.47 *|1.28 *<br>0.58 *<br>-0.15<br>-0.59 *<br>-0.20|Fail to reject<br>Null<br>Hypothesis|No|0.36 **||No||108|
|5)|rule<br>oil<br>landlock<br>Sub-Saharan Africa<br>malaria|eurfrac|0.80 *<br>0.35 *<br>-0.22 **<br>-0.59 *<br>-0.65 *|1.18 *<br>0.52 *<br>-0.15<br>-0.55 *<br>-0.45|Fail to reject<br>Null<br>Hypothesis|No|0.38 **||No||86|
|6)|rule<br>Log(patents per capita)<br>oil<br>landlock<br>Sub-Saharan Africa<br>malaria|eurfrac<br>lip|0.50 *<br>0.15 *<br>0.40 *<br>-0.34 *<br>-0.44 *<br>-0.41 **|-0.01<br>0.23 *<br>0.26<br>-0.47 *<br>-0.41 *<br>-0.49|Fail to reject<br>Null<br>Hypothesis|No|rule<br>0.37 **<br>0.04|lpats<br>1.75 *<br>1.26 *|No<br>No|0.14<br>0.17|86|

Notes: Confidence Levels: *5%, **10%. All regressions include a constant, dummy for net exporters of non-oil commodities, absolute value of latitude, and the log of Frankel and Romer's (1999) constructed trade shares.

The results reported in the first two rows show that the results reported in most studies that followed Acemoglu et al. (2001) are reproduced with our data and sample. The impact of rule of law on income per capita is positive and significant in both OLS and IV estimates. Also, the IV coefficient is larger than the OLS coefficient, which might indicate that the OLS estimates suffer from attenuation bias possibly due to measurement error in the rule of law indicator. Furthermore, the Hausmann specification tests suggest that the whole set of estimated 21
Source: Ministry of Economy and Finance of Peru, Quarterly Report as of December 31, 2016.

Peru had not issued in EUR for 10 years until a change in the macro fundamentals triggered the issue of EUR 1.2 billion in 2015. The justification for the change in the strategy is partly related to the new context for the international economy: the slowdown in China and the severe fall in commodity prices have resulted in a sharp contraction of the current account surplus and in the government external revenues both of which have widened the fiscal deficit and increased the funding requirements, particularly in foreign currency.

**Figure 8: Peru Foreign Currency Bond Issuance by Year**

<!-- Start of picture text --> 2000<br>1800<br>1600<br>1400<br>BBB+<br>BBB<br>1200<br>BBB‐<br>1000<br>BB+<br>800<br>600<br>400<br>200<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>EUR USD LT FX Credit Rating<br>Millions in USD Eq.<br><!-- End of picture text --> Source: Bloomberg, Standard and Poor’s.

The increase in the funding needs coincided with a contraction in the demand for government securities both in foreign and local currencies and an upward movement in the government yield curves. Since mid-2015 foreign investors experienced a significant reduction in the appetite for risk, possibly as a consequence of the gradual reversion of monetary policy by the FED. As capital started flowing out, the Central Bank tightened liquidity conditions to help contain the depreciation of the exchange rate, completing the picture to explain the upward movement in the LX and FX yield curves.

Macro and market conditions have increased the dependency of government borrowing from foreign investors, highlighting the need to diversify the investor base. Peru has traditionally been highly dependent on non-resident investors, who hold 37.6% (as of December 2016) of government securities in local currency plus a large part of the international bonds in USD. With expectations of increasing borrowing requirements, Peru has prioritized the diversification of funding sources, including accessing the EUR market.

The government achieved significant diversification with the placement of an 11-year EURdenominated bond in 2015. According to the authorities, only 25% of the investors were traditional buyers of USD bonds, while 75% were new investors. The authorities plan to continue tapping the EUR market to guarantee continued access and to consolidate a more diversified investor base (a 15
rate. Trade balance also becomes much less countercyclical, but its volatility rises (by around 20% relative to the benchmark case with �=1). This is primarily driven by higher volatility of investment.

These changes occur because without the working capital requirement, the negative impact of interest rates on labor demand of firms is eliminated. As a result, it becomes easier for firms to adjust employment and investment in response to productivity shocks. This leads to higher volatility of employment, investment and, therefore, trade balance. At the same time, firms’ employment decisions become less sensitive to interest rate shocks when �=0, which lowers the volatility of employment in the model. The second effect dominates and employment volatility is reduced with lower �. Volatility of investment, however, unambiguously rises.

When �=0.5, the same results apply, although the changes in volatilities and correlations are smaller. Importantly, the correlation between GDP and interest rate remains negative at -0.21 when �=0.5, and trade balance remains countercyclical. Consumption and investment also commove negatively with the interest rate when � is halved. Thus, only larger values for � are consistent with the business cycles properties of the OECS countries.

**<u>Table 9. Sensitivity analysis</u>**

|||% S<br>% Standa<br>|tandard<br>rd deviati<br>|deviation<br>on of x,<br>|of x<br>benchmar<br>|k<br>||Correlation be<br>|tween<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||GDP|TB/GDP|Inv|Gov<br>exp|Cons|Employ|GDP,R|GDP,TB/GDP|Cons,R|Inv,R|
|**1. Indep**|**endent**|**fiscal polic**|**y**||||||||
|�**=0.5**|0.90|1.05|1.36|0.62|0.84|0.80|-0.20|-0.21|-0.44|-0.69|
|�**=0**|0.86|1.19|1.70|0.36|0.76|0.70|0.06|-0.05|0.05|-0.70|
|**2. Pro-c**|**yclical f**|**iscal policy**|||||||||
|�**=0.5**|0.90|1.07|1.36|0.62|0.84|0.80|-0.20|-0.27|-0.46|-0.69|
|�**=0**|0.86|1.21|1.70|0.36|0.78|0.70|0.06|-0.07|0.05|-0.70|

_Note_ : The table reports the volatility of various aggregates relative to the volatility of the corresponding aggregate under the benchmark parameterization of �=1. All other parameters are set to their benchmark values given in Table 5. _Source_ : Authors’ calculations.

These results suggest a few steps that policy makers in these economies could take to help cushion the effects of the shocks that their economies experience. For instance, the importance of access to international borrowing and lending in smoothing the effects of productivity shocks underscores the importance of greater financial openness for the OECS countries. This greater openness, however, must be accompanied by the development of the domestic financial markets and policies aimed at stabilizing country risk-premium. To the extent that government’s procyclical policies contribute to the country risk-premium demanded by international markets, the government can help stabilize the country risk-premium by switching to countercyclical policy rules, and thus reduce the interest rate shocks faced by the private sector. The development of wellfunctioning domestic financial markets can also contribute positively to reducing the negative 27
- 4 -

over time, we analyzed, two tima periods: 1972-1981 and 1982-1992. The expectation is that the Bank may have paid greater attention to gender equity in projects in the more recent period.

Further material was extracted from Project Completion Reports (PCRs) and the Project Performance Audit Reports (PARs) and servei as an evaluative tool for assossing project success in attaining the gender obJectives stated in the SARs. To analyze the preliminary results of recent on-going projects with gender specific interventions, we interviewed project staff for selected countries.

# Limitations of the Study

Notwithstanding the comprehensive coverage, the study has certain limitations in the selection and evaluation of projects. First, the study is limited only to the Bank's experience from 253 education projects with higher education components between 1972-1992. As such, it does not capture non-Bank project experience.

Second, limited gender awareness does not necessarily indicate a significant gender parity problem. For instance, in the Philippines, near parity in higher education enrollment exists. Not surprisingly, projects in the Philippines do not refer to the existence of a gender problem.<sup>2</sup> On the other hand, in th". Gambia, where gender disparity at the tertiary level is great, failure to recognize the inequity would indicate low gender-sensitivity in the Bank-assisted higher education projects.

Analyzing individual projects without understanding the government's overall plan for post-secondary education is not entirely satisfactory. In countries where primary enrollment rates remain excessively low, policies may focus on increasing access to and expansion of basic education, with less regard to gender parity in higher education. With these caveats, lets us examine the trends in Bank lending to higher education.

> 2 However, even in these countries, women may be opting for low-paying, traditional fields of study. This issue of gender streaniing could not be effectively addressed in this paper owing to insufficient data base.
Table 5: Participation in SUF by initial conditions, for all cohorts pooled together (2002-2006) and separately (2002-2004 and 2005-2006).

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Years after start|2|4||2|
|Cohort|||2002-2004|2005-2006|
||Panel A|: Not recei|vingSUF bef|ore 2002|
|Eligibility (ITT)|0.034***<br>(0.013)|0.040**<br>(0.018)|0.018<br>(0.015)|0.071***<br>(0.026)|
|Participation (IV)|0.180***|0.220**|0.106|0.318***|
||(0.064)|(0.096)|(0.085)|(0.116)|
|Control Mean|0.494|0.541|0.481|0.524|
|Observations<br><br>|50,605|28,306|35,277|15,328|
|P-Value: HA:β<sup>_iv_</sup><br>0506 <sup>_>_ β</sup><sup>_iv_</sup><br>0204|||0.0|60|
||Panel|B: Receivi|ngSUF befo|re 2002|
|Eligibility (ITT)|0.019**|0.002|0.020**|0.016|
||(0.008)|(0.013)|(0.009)|(0.019)|
|Participation (IV)|0.091***|0.015|0.103**|0.055|
||(0.035)|(0.067)|(0.047)|(0.065)|
|Control Mean|0.762|0.745|0.765|0.753|
|Observations|65,558|37,865|48,215|17,343|
|P-Value: HA:β<sup>_iv_</sup><br>0506 <sup>_>_ β</sup><sup>_iv_</sup><br>0204|||0.7|36|
|P-Value: HA:β<sup>_iv_</sup><br>0 <sup>_>_ β</sup><sup>_iv_</sup><br>1|0.164|0.000|0.496|0.008|

Note: See table 3 for a description of specification used in rows (ITT) and see table 4 for the description of specification used in rows (IV). The coefficient estimate in rows (ITT) is the indicator of eligibility, _Eim_ , and that in rows (IV) is the indicator of participation in CS, _CSim_ .

The p-values in panels A and B concern the null hypothesis that the effect for those that entered in CS in 2005-2006 equals the effect of those that entered in 2002-2004, H0: β<sup>_iv_</sup> 0506<sup>= β</sup><sup>_iv_</sup> 0204<sup>, against</sup> the alternative that the effect is larger for those entering in the later year, HA: β<sup>_iv_</sup> 0506<sup>_>_β</sup> 0204<sup>_iv_.</sup> The p-value in the last row tests the null hypothesis of the effect for those without SUF prior 2002 equals the effect on those receiving SUF, H0: β<sup>_iv_</sup> 0<sup>= β</sup><sup>_iv_</sup> 1<sup>,againstthealternativethattheeffectis</sup> larger for those that did not receive SUF prior to 2002, HA: β<sup>_iv_</sup> 0<sup>_>_β</sup><sup>_iv_</sup> 1<sup>.</sup> Robust standard errors are reported in parenthesis clustered at municipality of residence when eligibility is evaluated. * significant at 10%; ** significant at 5%; *** significant at 1% (critical values for inference are adjusted for multiple hypotheses testing).

30