rafmacalaba/datause-encoder-data huggingface dataset:
dollar an explicit fund receives is associated with 84 cents allocated proportionally to the benchmark weight. This number declines for funds that are more active, being 0.69, 0.55, and 0.41 for closet indexing, mildly active, and truly active funds, respectively. The relationship is also maintained when we control for different sets of fixed effects. Under this estimation, a change in the benchmark weight changes the sensitivity of country flows to fund flows as indicated above.

A corollary of Equation (12) and the estimates in Figure 6 and Table 10 is that changes in benchmark weights change the sensitivity of country flows to fund flows (which can be derived by taking a second derivative of Equation (12) with respect to benchmark weights). This leads to interesting dynamic interactions between various effects. For instance, a decline in the returns of the rest of the countries sharing a benchmark with country A will induce a higher benchmark weight for country A. But the same increase in benchmark weights makes country A more vulnerable to future movements in fund flows. If in reaction to the initial shock there are large withdrawals of funds, country A would be more affected even though it was the country that performed relatively well. Namely, during good times (when funds are receiving injections), a country that does relatively well gets more country flows. During bad times, a country that does relatively poorly (its weight decreases) is less affected by the outflows.

Some of these benchmark effects can be illustrated by the evolution of country flows to China and Russia from explicit indexing funds following the MSCI Emerging Markets Index, before the global financial crisis and during the stronger phase of the European crisis (Figure 7). We also show a similar analysis for Spain and Ireland in the explicit indexing funds tracking the MSCI Europe, Australasia, and, Far East index. Before the global financial crisis China and Russia had similar benchmark weights and flows. However, during the global financial crisis China did relatively well compared to Russia, which increased its benchmark weight importantly. During the peak of the European crisis, emerging market funds had net withdrawals, which translated into much larger outflows in China than in Russia (proportionally to their weights). That is, China was penalized by its stronger performance. A similar pattern is observed for developed countries. Spain and Ireland received inflows during the pre-crisis, with the former receiving four times more flows than Ireland according to its benchmark weight. Still, Ireland received around 80 million U.S. dollars in that period. Immediately after the crisis, Ireland did relatively worse than Spain, and the subsequent outflows were smaller in Ireland than in Spain.

26
cohorts.<sup>38</sup> In a reduced form graph, shown in in the lower right panel of Figure 5, and in Panel B of Table 4, I look at the probability of pregnancy by age 18 among female KLPS2 respondents. The association between secondary schooling and decreased early fertility is strong: in the last two columns, OLS shows a roughly twelve percentage point drop in teen pregnancy among secondary school finishers. While these are only cross-sectional associations, their sign agrees with associations seen in Colombia; Taiwan, China; and the United States; summarized by Schultz (1988). Two-stage least squares predicts outside the unit interval, since again, this is a low-probability outcome, so I use IV probit and bivariate probit estimation in the first four columns and find a near elimination of teen pregnancy among compliers at the discontinuity, robust to the inclusion of the usual controls.

This finding contrasts with the work of McCrary and Royer (2011), who find no conclusive effect of education on timing of womens’ first births. As McCrary and Royer (2011) point out, however, their study is based on a manipulation of the age at school entry rather than the age at school exit, as is the case here. In effect, when a girl starts school one year earlier than her counterparts because her birthday falls before a cutoff date, she has one more year of education by the time she considers dropping out of school at a particular age, perhaps in relation to the legal minimum. Their date-of-birth instrument thus predicts educational attainment among those who, for the most part, do not go on to tertiary schooling and in fact stop schooling al-

> 38At the discontinuity, men appear slightly less likely to be married by survey time, and women appear slightly more likely to be married, but neither effect is significant. Conditional on marriage, spouse education rises slightly at the discontinuity (as one might expect), but this effect is also statistically insignificant (results not shown).

25
- 23 -

# Table _2.5_ Summary of Typical Joint Switching Agreement

<!-- Start of picture text --> Basic Concept<br>Railways A, B, and C agree to share the switching responsibilities at a major industrial plant; each road<br>will do the switching for all three, for a four-month period. [Ownership of the track here is evidently<br>divided between the plant, and Railways A, B, and C.]<br>Contents of the Agreement<br>Identification of Facilities<br>Responsibility of the Switching Railway:<br>* Provide crews and locomotives and switch all cars to, from, and within the plant.<br>* The switching road will 'give no preference" to its own traffic. [The rotational scheme helps to<br>assure this; see text.]<br>Revenues:<br>* Will always accrue to the carrier on whose behalf the cars are switched.<br>Expenses:<br>* Variable costs only are allocated.<br>* Costs covered:<br>* Engines and supplies at standard rate (here $7.50/hour).<br>* Engines and switching crews: standard rate paid each employee.<br>* Allocation formula: Based on each road's projected share of loaded cars.<br>Liabilities:<br>* Very complex; occupies 50 percent of agreement's length and provides for every contingency.<br>* Under certain circumstances, liability is allocated in proportion to car counts for the six months<br>preceding an accident.<br>Source: Wheeling & Lake Erie Railway Co., Pennsylvania Railroad Co., and Baltimore & Ohio Railroad<br>Co., joint switching agreement for Massillon, Ohio, July 20, 1932.<br><!-- End of picture text -->
<!-- Start of picture text --> Depth All Depth LE<br>©|a|<br>t !_ | ae<br>| et =t “<br>“ a | ff<br>| “ tg le _<br>“ a _ oer| Steere TM ||<br>o+= = o+ ——<br>~~ -__! ee ~ | Pa<br>-2 -1 0 1 2 3 a -2 -1 0 1 2 3 4<br><!-- End of picture text -->

<!-- Start of picture text --> Depth Core All Depth Core LE<br>©|&|<br>||<br>~ ~<br>| |<br>| ae | aoc __<br>° = a ° = os<br>“ N<br>-2 -1 0 1 2 3 = -2 -1 0 1 2 3 4<br><!-- End of picture text -->

Depth 1 year prior to agreement;;, = Depthij(¢41) * IPT Aijt
treatment indicator and the interaction of this variable with a dummy for girls. Similarly, in Panel B the treatment indicator is interacted with a dummy that signals that the school has a higher enrollment than the median school. Panels C and D report results from analogous regressions when splitting the sample by prevalence of indigenous population and by the share of adults with no education. Finally, in Panel E we explore the extent to which malnutrition could be attenuating the positive effects of pre-primary attendance. We matched data at the municipality level from the Second National Census of Height of 2001 ( _Segundo Censo Nacional de Talla_ ). They measured the height for all first grade students aged 6 through 9, constructing an indicator of prevalence (%) of stunted children at the municipality level.<sup>10</sup> There are several noteworthy findings. First, we find that the effect of pre-primary attendence does not appear to differ significantly by gender (Panel A). Documenting similar effects between genders is in line with the aforementioned studies for Argentina and Uruguay (Berlinski et al., 2008; Berlinski et al., 2009). However, evidence from the United States document larger effects of preschool programs for girls (Oden et al., 2000; Anderson, 2008; Cascio, 2009). Second, we do not find consistent differences in effects on enrollment when classifying communities by percentage of indigenous population, share of adults with no education or prevalence of chronic malnutrition (Panels C to E); .

Similar to the effects on enrollment, when considering progression we find no statistically significant differences for populations with higher proportions of indigenous population or with a high prevalence of chronic malnutrition. However we do find that the positive effects of pre-primary access are attenuated in communities where a high share of adults has no education. This may be due to several factors associated with higher educational attainment in the community. For example, pre-primary take up rates may be higher in communities with higher education since it may be valued more by parents. Nevertheless, this does not seem to be the case since we do not find relevant differences in take-up rates between communities with high and low shares of adults with no education.<sup>11</sup> Alternatively, these estimates may reflect positive interactions between pre-primary attendance and parental education, or the fact that teacher quality is likely to be higher in communities with higher average education.

# **6 Robustness**

For robustness, we address several potential challenges to our difference-in-differences estimates. To assess the validity of the basic identification assumption, we then test for the

> 10In our sample, the mean prevalence is around 50%.

> 11The average take-up is 60.1% and 57.4% for low and high share of adults with no education (p-value: 0.237).

12
The results in Table 7 Panel A point to some significant differences across firms with and without female ownership participation in terms of size and tendency to innovate. The results in Columns 1-4 suggest that there are important differences between enterprises with and without female ownership participation in terms of size: all things equal, firms with female ownership participation tend to be significantly smaller, a result that holds both for the overall sample and the sub-sample of sole proprietorships.<sup>7</sup> Having female ownership participation reduces the number of employees, on average, between 8 and 14 percent. Having found that size is a key factor positively correlated to access to finance, this may explain how, by being small, enterprises with female ownership participation tend to be less likely to access finance, while at the same time, we do not find any statistically significant difference between businesses with and without female ownership participation once we control for size. Interestingly, African firms are, on average, not smaller, while sole proprietorships are smaller in Africa. The interaction of the African and female dummy variables is insignificant, suggesting that the smaller size of firms with female ownership participation is not more or less pronounced in Africa than in other parts of the world. In addition, the results in columns 5-12 show that enterprises with female ownership participation are significantly more likely to innovate than enterprises without female ownership participation, both with respect to product and process innovation. On average, firms with female ownership participation are three to six percent more likely to innovate than other firms. These results hold not only for the overall sample of enterprises, but also for the sub-sample of sole proprietorships, though the significance of the female dummy drops to 10 percent in the regressions of process innovation (columns 11 and 12). In addition, the interaction terms between the Africa and the female dummy variables enter negatively and significantly in the regressions for sole proprietorships, suggesting that female-owned

> 7 Bruhn (2009) reports similar findings for a sample of Latin American countries.

15
> Table l b: Probit results of the probability of enrollment into Secondary Education: Albanian Females in comparison with Serbs

||Albanian <br>|Females:A<br>|ge16-26<br>|Albanian <br>|Females:A|ge**27-35**|Serb Males|& Females:|Age:16-35|
|---|---|---|---|---|---|---|---|---|---|
||Coefficient<br>|Z<br>|Mean|Coefficient|Z|Mean|Coefficient|Z|Mean|
|Ageis16years<br>|0.976<br>|6.418<br>|0.106<br>|||||||
|Ageis24years<br>|0.072|0.513|0.097||||-<br>|-<br>|-|
|Ageis27-35years<br>|-||||||-0.113<br>|-0.558<br>|0.331<br>|
|Females<br>|-<br>||||||-0.216|-1.083|0.524|
|Serbsandothers<br>|-0.009<br>|-0.041<br>|0.045<br>|0.234<br>|0.920<br>|0.054<br>|-1.400|-1.998|0.008|
|BominUrbanareas<br>|1.036<br>|11.466<br>|0.389<br>|0.885<br>|7.567<br>|0.294<br>|0.465|1.777|0.356|
|BornoutsideFormerYugoslavia<br>|0.538|0.639|0.003|-0.059|-0.125|0.017|-|-|-|
|ResidenceoutsideFormer<br>||||||||||
|Yugoslaviabeforeconflict-<br>|-<br>|-<br>|-|0.202|0.588|0.025|-|-|-|
|Father'seducation(inyears)<br>|0.076|6.298|10.485|0.022|1.517|10.167|0.084|3.179|10.703|
|ValueofHouseholddurables<br>||||||||||
|over5yearsold(log)<br>|0.030|0.888|6.551|-0.038|-0.879|6.798|0.321|2.863|7.236|
|AreaofResponsibility<br>||||||||||
|France<br>|-0.460<br>|-3.176<br>|0.114<br>|-0.232|-1.235|0.098|-0.214|-0.791|0.517|
|Germany<br>|-0.784<br>|-6.048<br>|0.204<br>|-0.823<br>|-5.293|0.246|-|-|-|
|Italy<br>|-0.557<br>|-4.239<br>|0.165<br>|-0.281<br>|-1.733|0.159|-|-|-|
|USA<br>|-0.353<br>|-2.937<br>|0.218|-0.366|-2.387|0.203|-0.417|-1.466|0.234|
|Constant|-1.038|-4.113||-0.151|-0.469||-1.330|-1.458||
|N<br>|1108|||642|||454|||
|Loglikelihood<br>|-596.850<br>|||-383.129<br>|||-98.452|||
|PseudoRsquare|0.222|||0.117|||0.195|||

Note: The variable "Serbs and others" is modified to "Albanians and others" in the Serb model.

26
of finance. Second, the data on project costs relates only to net capital expenditures and excludes other uses of funds by firms.

As noted before, this data on financing of project costs can be compared to the **estimates** of Singh and Hamid (1992) and Singh (1995). However, in practice, the estimates shown in tables 5 and 10 turn out to be different, possibly due to methodological differences discussed earlier.' It is interesting to note that the 28 percent for loans from financial institutions compared to the 11 percent for loans from banks in table 10 are consistent with the earlier figures shown in tables 2, 3, and 4 and highlights the critical role played by development banks in Indian capital markets.

The comparison of project financing patterns of existing (table 9) and new (table 8) firms provide some interesting insights. While internal finance and debentures played a much greater role in project financing for existing companies, extemal equity and loans from FIs played a much greater role for new companies.25 Since agency costs are likely to be greater for existing firms than new firms, the greater reliance of existing firms on internal finance can be viewed as broadly supportive of the managerial theory of investment in the Indian context.26 In contrast, the information-theoretic approach would have predicted existing firms to be less dependent on 4 A comparison of tables 5 and 10 for the post-1980 period suggests that the figures in table 10 are closer to the upper quartile firms in table 5 than the median or the mean firm.

5 This is consistent with the evidence in RBI (1995). Based on the analysis of capital issues for the 1986-87 to 1990-91 period, RBI (1995) found (convertible) debentures to be the primary source of capital for existing companies and new equity shares to be the primary source of capital for new companies.

' In this context, it is interesting to note that Athey and Laumas (1994) and Cobham and Subramaniam (1995) find internal finance to be relatively more important for large Indian firms than small firms. This evidence is consistent with the results presented here, once it is recognized that existing firms are likely to be bigger than new firms that are usually small.

24
51 _Table 3.4: Indonesia: SelectedMacroeconomicindicators, 1980-85_ **_(Rp.bIllon)_**

||1980|1981|1982|1983|1984|1985|
|---|---|---|---|---|---|---|
|GDP (1983prices)|66,674|71,613|71,377|73,698|78,214|79,046|
|(Percent<br>gowth)|-|7.4|-0.4|<br>3.25|6.1|1.06|
|Totl rvenmis|10,227|12,213|12,418|14,433|15,905|19.253|
|(Pautoth)|-|19.4|1.7|16.2|10.2|21.0|
|_Change_**_in_**_Price_**_Index(p.a.)_**|||||||
|'WholcswlIcprice ucx|-|-|-|-|11.0|<br>4.5|
|CPI|16|7.1|9.7|11.5|8.8|4.3|
|Food|-|8.71|7.5|10.4|6.5|2.0|
|Housing<br>|-|8.1|16.0|13.5|13.5|7.2|
|<br>Others|-|6.1|12.1|17.1|11.3|5.4|

The GDP gowth duinghis period was lower than the 1970s. Since 1982, falling intemational oil prices and ot rev es for Indonesia created a need for macroeconomic policies focussed on demand rstrintto maintain financial stability, a goal attaied at the cost of a fall in GDP growth rates during the period. However, these short-term policies were complemented by structural policy reforms to improve efficiency in economic activities. Domesfic energy prices contributed to both these short- and long-term objectives. Although it is difficult to isolate the effects of energy price increase in a macroeconomic environment ad a number of policy changes were being implemented at the same ime, the positive revue effect and effient prncng of energy iputs contrbuted to the achievement of short-, medium- and longtern objectives of policy makes.
the home market and abroad, with potentially beneficial ramifications on the cost of capital (see Brennan and Subrahmanyam, 1996; Werner and Kleidon, 1996; Domowitz, Glen, and Madhavan, 1998; Aggarwal, Dahiya, and Klapper, 2007; Baruch, Karolyi, and Lemmon, 2007; and Halling, Pagano, Randl, and Zechner, 2008).

The market timing view suggests that firms raise capital abroad to exploit temporarily high prices for their securities during „„hot‟‟ markets (Errunza and Miller, 2000 and Henderson, Jegadeesh, and Weisbach, 2006). Henderson, Weisbach, and Jegadeesh (2006) find evidence of market timing in both equity and debt markets – firms issue equity when the stock markets appears to be overvalued and time long-term debt issuances when interest rates are lower, and prior to increases in interest rates. They also show that firms are drawn to liquid and well regulated markets such as the U.S. and U.K. for new security issuance. Kim and Weisbach (2008) also find that firms issue public equity to raise capital for investment and to exploit favorable market conditions (market timing).

The „„bonding‟‟ hypothesis argues that firms internationalize to bond themselves to a better corporate governance framework. For instance, firms cross-listing in the U.S. are subject to U.S. securities laws and disclosure rules, exchanges‟ listing requirements, and additional monitoring by reputational intermediaries, such as analysts, institutional investors, and underwriters. Thus, by bonding to better corporate governance standards that limits the extraction of private benefits by corporate insiders, firms look more attractive to potential investors (Stulz, 1999; Coffee, 2002; Reese and Weisbach, 2002; and Doidge, Karolyi, and Stulz, 2004).

However, the bonding hypothesis also implies that controlling shareholders who have a large proportion of voting rights compared to cash flow rights and have a greater ability to extract private benefits are less likely to list in the U.S. Doidge, Karolyi, Lins, Miller, and Stulz (2009) investigate this proposition using ownership and control data on more than 4000 firms from 51 countries. They put together an extensive dataset consisting of cash flow and control rights held by the controlling (largest) blockholder in each company as well as the control rights held by a firm‟s officers, directors, top-level managers, and their family members. They use 45
||||||such increases are quite<br>strong and negative|
|---|---|---|---|---|---|
|Clark et al.<br>(2005)|case study|Africa|energy sector<br>indicators,<br>macro<br>variables|multi-<br>country|impacts of reforms on<br>the poor are neither<br>direct nor inevitable|
|Betily,<br>Movchan<br>and<br>Pugachov<br>(2013|macro study|Ukraine|household<br>survey data for<br>2009|CGE<br>model|increases in gas prices<br>result in welfare losses<br>across all household<br>categories, with a more<br>profound impact on<br>urban households|
|Solaymani,<br>Kari and<br>Zakaria(201<br>3)|macro study|Malaysia|time series<br>macro and<br>micro data|CGE<br>model|subsidy removal can<br>potentially lead to<br>significant falls in<br>income of rural<br>households leading to<br>rising poverty levels<br>among the rural<br>households|
|ADB (2005)|case study|Asia|macro-micro<br>data|multi-<br>country|strong links between<br>investments in energy<br>infrastructure and rural<br>poverty reduction in<br>Thailand and India|
|Estache,<br>Foster and<br>Wodon<br>(2002)|case study|Latin<br>America|macro-micro<br>variables|multi-<br>country|evidence of reforms on<br>poverty reduction is<br>scarce; hence the<br>analysis remain<br>incomplete|

# **4. Discussions and Policy Implications**

A review of the limited literature and evidence on the impacts of electricity sector reforms suggests a growing trend in this research area among the developing and transition countries. The existing reform studies have used both quantitative and qualitative techniques to assess the impact of reforms with no strict preference over one another. Latin America remains the most studied region in the reform literature for two major reasons: a) being one of the pioneers of the market-driven electricity reforms and b) the ability of the Latin American Association of Energy Regulators to generate comparable data.

38
**Figure 2: Distribution of estimated slack variables for repeating markets by year of survey**

<!-- Start of picture text --> 1995 1997<br>0 .5 1 1.5<br>2001<br>0 .5 1 1.5<br>Density kdensity slack<br>Graphs by survey<br>15<br>10<br>5<br>0<br>15<br>10<br>5<br>0<br><!-- End of picture text --> 24
# **Table 2**

|||Mexico|Brazil|Colombia|||
|---|---|---|---|---|---|---|
|Null|Alternative|Lag: 4|Lag: 4|Lag: 2|95%|90%|
|Hypothesis|Hypothesis|With Constant|With Constant|With Constant|Critical Value|Critical Value|
|λ tracetest|||||||
|r = 0|r > 0|55.80<sup>*</sup>|40.56<sup>*</sup>|36.45<sup>*</sup>|35.10|31.88|
|r≤1|r > 1|12.08|19.68|6.43|20.17|17.79|
|r≤2<br>|r > 2<br>|4.04<br>|6.05|1.69|9.10|7.50|

<u>*Rejection at the 5% level of significance 18</u>

# **Table 3**

||**Mexico**|**Brazil**|**Colombia**|
|---|---|---|---|
||_β_|_β_|_β_|
|nT/nN|1.000|1.000|1.000|
|WT/WN|0.969|-0.637|2.846|
|P|0.976|-0.356|-2.776|
|Constant|-2.803|0.101|-4.811|

# **Table 4**

||**México**||**Brazil**||**Colombia**||
|---|---|---|---|---|---|---|
||_~~α~~_|t-stat.|_~~α~~_|t-stat.|_~~α~~_|t-stat.|
|∆nT/nN|0.028|0.531|-0.012|-0.212|-0.213|-1.750|
|∆WT/WN|-0.088|-3.393|0.572|3.841|-0.057|-1.961|
|∆p|-0.250|-6.457|0.281|2.381|0.088|6.009|

_Note:_ ∆ indicates a variable in first differences.

**Table 5: Summary of Hypothesis Tests (whole sample)**

||**Mexico**|**Brazil**|**Colombia**|
|---|---|---|---|
|**Integrated**|2<br>_χ_ (1) = 15.42|2<br>_χ_ (1) = 1.87|2<br>_χ_ (1) = 16.96|
|**(1,-1,*,*)**|p=0.00|p = 0.17|p = 0.00|
|**Segmented**|2<br>_χ_ (1) = 0.04|2<br>_χ_ (1) = 7.26|2<br>_χ_ (1) = 5.13|
|**(1, 1,*,*)**|p=0.84|p=0.01|p = 0.02|

**Table 6:** Test of (1,-1,-1,*) being part of the cointegration space in different subsamples

||México|Brazil|
|---|---|---|
|Sample:|**1988:01– 1993:04:**|**1982:01 – 1998:02:**|
||2<br>_χ_ (2) = 4.35 , p=0.11|2<br>_χ_ (2) = 5.06, p = 0.08|

18 The Reinsel-Ahn small sample corrected critical value at the 10% level is 43.13 for Mexico, 39.53 for Brazil and 36.32 for Colombia).

34