rafmacalaba/datause-encoder-data huggingface dataset:
According to these arguments, the impact of contractual savings development on firms’ financing choices can be assessed by considering that (1) it leads to an increase in the supply of long-term debt, (2) it reduces equity rationing, (3) it fosters information disclosure on the stock market.

The theoretical predictions imply that the development of contractual savings institutions will be more beneficial when projects are illiquid, when the banking sector is fragile and highly competitive. Although information disclosure tends to increase the maturity of debt, the impact of increased access to the equity market is ambiguous: it may either increase the maturity of debt, or decrease it, depending on whether firms initially have few liquid reserves or not.

The equilibrium capital structures of firms will be a function of (1) their characteristics (maturity of assets, profitability, risk, asymmetry of information, etc.), (2) the efficiency of the financial system (for instance in generating - ex-ante and interim - private and public information), (3) the supply of funds to capital markets, that are affected by the nature of investors.

# **4. Data and Empirical Strategy**

## **_4.1 Data_**

We use data from Worldscope, a firm-level database that has been widely used in recent papers. It includes publicly listed corporations in 54 countries. Our sample includes all companies except financial firms. We use an unbalanced sample of firms over the period 1989-1998, in order to maximize the time-series dimension for each country. We require having data on at least 10 firms per year in each country.<sup>50</sup> The database on contractual savings is taken from Impavido and Musalem (2000), extended for several countries (Argentina, Brazil, India and Mexico). It includes information from different sources (including the OECD institutional investors database, and national sources) on total assets, financial assets, and allocation of assets for pension funds and insurance companies.

All other macroeconomic variables are obtained from various sources: Datastream, the World Development Indicators, and the Bank for International Settlement for bond markets data and the database constructed by Beck and al. (2000).

> 50 Except for Luxembourg for which we have only 9 firms, and one single year of observation  (in 1995) for the contractual savings data (so this country is dropped in all panel estimations).

18
Table 4 also shows that matching services were made available almost exclusively to jobseekers in the treatment group, in line with the experimental design. More than 80% of jobseekers in treatment benefited from general matching. The corresponding number for the control group was 7.5 percent (column 4). The reason for this spillover was that during the design it was agreed that while matching services would be restricted to the treatment group, if any jobseekers from the control group became aware about and requested these services, they would be provided to them as well. Specific matching (which involved pre-selecting candidates and matching them with employers in response to specific needs of the firm) was provided to 23 percent of jobseekers from the treatment group and only a single jobseeker from the control group (column 4).

# **5.3. Main Treatment Effects and Heterogeneity**

The overall intention-to-treat effects of assignment to matching services on short- and medium-run employment outcomes are presented in Table 5. The first two columns include controls from administrative data, whereas the last two include additional control variables collected using the baseline survey (and therefore only include individuals who responded to the survey). Matching services increased the likelihood of formal employment by 5.8-8.8 percentage points (pp) in the short run. The effects are smaller over the medium run but remain positive and significant at 4.1-6.9pp. Given that 22.9 percent and 17.9 percent of jobseekers in the control group were employed in the short and medium run respectively, these effects translate to an approximately 25 percent increase in employment probability in the shortrun, and 22.6 percent over the medium-run.

Next, we examine heterogeneity of the treatment effects along five dimensions – gender, age, level of education, length of unemployment and _“employability”_ level (as determined by the job counselor). Table 6 shows that the treatment effects in the short and medium run are large, positive, and significant for men at around 12.9pp. For women, the treatment effects, while positive are much smaller in magnitude and statistically indistinguishable from zero.

Turning to heterogeneity by age in Table 7, we find that access to matching had large effects for jobseekers under the age of 30 with treatment effects of 9.3pp and 7.8pp in the short and medium run respectively. For older jobseekers, the effects were much smaller and statistically indistinguishable from zero. Similarly, Table 8 shows that access to additional matching services increased the likelihood of 13
# FIGURE 1 Estimates of potential growth

A. Advanced-economy average annual potential growth (range across methodologies) Percent <!-- Start of picture text --> 3<br>2<br>1<br>0<br>2000-10 2011-21<br><!-- End of picture text --> C. Methodologies generating highest and lowest estimates of potential growth <!-- Start of picture text --> Percent of country-year pairs<br>PF MVF UVF Forecasts<br>100<br>80<br>60<br>40<br>20<br>0<br>2000-10 2011-21 2000-10 2011-21<br>Highest Lowest<br>E. Standard deviation of potential growth<br>estimates, 2000-19<br>Percent PF MVF UVF Forecasts<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>World Advanced economies EMDEs<br><!-- End of picture text --> B. EMDE average annual potential growth (range across methodologies) <!-- Start of picture text --> Percent<br>8<br>6<br>4<br>2<br>0<br>2000-10 2011-21<br><!-- End of picture text --> D. Uncertainty in global potential growth <!-- Start of picture text --> Percent MVF MVF CI<br>8 UCM UCM CI<br>6<br>4<br>2<br>0<br>-2<br>2000 2003 2006 2009 2012 2015 2018 2021<br><!-- End of picture text --> F. Persistence in potential growth estimates, 2000-19 <!-- Start of picture text --> Percent<br>1.2 PF MVF UVF Forecasts<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>World Advanced economies EMDEs<br><!-- End of picture text --> Source: World Bank.

Note: “PF” stands for production function approach, “MVF” for multivariate filter, “UVF” for univariate filter, and “Forecasts” for five-year-ahead growth forecasts from the IMF World Economic Outlook. “EMDE” = emerging market and developing economies. Aggregates refer to weighted averages ( constant real GDP weights at average 2010-19 prices and exchange rates).

A.B. Blue bars denote production function-based estimates. Orange whiskers indicate the range of eight estimates.

C. Graph shows the share of country year pairs during each period in which each methodology generates the highest or the lowest estimate of potential growth. Only country-year pairs are considered for which at least two methodologies are available. “UVF” stands for any of four univariate filters (Christiano-Fitzgerald filter, Baxter-King, Hodrick-Prescott, or Butterworth). Unbalanced sample of 30 advanced economies and 25 EMDEs for 1998-21.

20
# Effect of Lengthening the School Day on Mother’s Labor Supply

_Dante Contreras and Paulina Sepúlveda_

**JEL Codes:** H42, J13, J22, O12

**Keywords:** Childcare, Chile, female labor supply, fertility, labor supply

Dante Contreras (corresponding author) is a professor in the Department of Economics, Universidad de Chile, Santiago, Chile; his e-mail address is dcontrer@fen.uchile.c <mark>l. Paulina Sepúlveda is a researcher at MINEDUC and Universidad de Chile; her e-mail address is</mark> pausepulv@gmail.co <mark>m. The authors acknowledge the funding provided by Center for Advanced Research on Education and the grant by Centre for Social Conflict and Cohesion Studies (CONICYT/FONDAP/15130009). They are also grateful for the comments made by Francisco Gallegos, Norbert Shady, and the participants of the workshops at Universidad de Chile, IADB, Chilean Economic Society, Universidad de Concepción, and Network Inequality and Poverty. The authors also thank Soledad Cabrera for her excellent work in preliminary versions of the paper. Any errors or omissions are the exclusive responsibility of the authors.</mark> Please cite the article published in the World Bank Economic Review: Contreras, D and P. Sepúlveda “Effect of Lengthening the School Day on Mother ́s Labor Supply” (forthcoming), <u>http://wber.oxfordjournals.org/.</u>
## Collecting Accurate Data on Intimate Partner Violence:

# Learnings from Pakistan<sup>∗</sup>

Nasir Iqbal Amen Jalal Mahreen Mahmud Kate Vyborny JEL codes: J12, C83, J16 ∗Iqbal: Pakistan Institute of Development Economics (nasir@pide.org.pk), Jalal: London School of Economics (u.jalal@lse.ac.uk), Mahmud: University of Exeter (m.mahmud@exeter.ac.uk), Vyborny: South Asia Region Gender Innovation Lab, World Bank (kvyborny@worldbank.org). We thank Amna Riaz for her incredible work in leading the survey activity and Maham Rasheed, Jaweria Waqar, and Sarim Zafar for their excellent research assistance. This project is funded by the Cash Transfer and IPV Research Collaborative and the IPV Initiative at Innovations for Poverty Action. We are grateful to Amber Peterman for her invaluable comments. This study has ethics approval from PIDE (RERC/2021-001), LSE (REC ref. 47614) and Duke (Protocol #2022-0207).
savings and investment model that they develop, they show that even those who did not borrow through the program would be expected to have benefited from it; because the program reduced the need for buffer stocks to guard against future low consumption.

In the Peruvian context, Karlan and Valdivia‟s (2006) randomized trial indicates potential benefits of supplementing microcredit programs targeted at female entrepreneurs with additional, general business training services, both in terms of improving the profitability of their firms and improving loan repayment rates.

In summary, these evaluation studies provide valuable evidence that microfinance programs have yielded high rates of returns, but that the returns have been typically lower if the programs have been too narrowly targeted. In designing a microcredit program, an important consideration is whether the cost of more specific targeting warrants the benefits in terms of redistribution toward women or toward the poor.

# **5  Programs That Facilitate Work through Lowering Costs of Work or Improving Working Conditions**

We next review the results of some evaluation studies of programs designed to facilitate women‟s work, either by providing better access to affordable and reliable childcare, by providing childcare subsidies or through more generous parental leave benefits. A program that subsidizes childcare has both an income and a substitution effect on women‟s labor supply. The income effect would be expected to reduce the number of hours she works, assuming that leisure is a normal good. The substitution effect would increase consumption of the subsidized good, i.e., increase the use of childcare and increase hours worked.

In this section, we also review evidence on the effects of Peruvian and an Argentinean land titling programs, which provide land titles to household squatting on public or private land. By providing legal protection for property, land titling programs significantly affect the wealth of these households and reduce the incentives for women and children to stay home. For this reason, land titling programs have been found to have substantial impacts on women working.

39
A second important set of studies on the United States using Gallup data shows an emerging partisan split between Democrats and Republicans (Dunlap, Xiao and McCright, 2001, and Dunlap and McCright 2008). On the one hand, this study provides updated data on a series of important questions including whether people believe that global warming is occurring, and whether they believe it will pose a serious threat within their lifetimes. The authors note that, “The percentage of Americans viewing global warming as a serious threat to themselves or their way of life during their lifetimes has moderately increased, from 25 percent in 1997 to 40 percent in 2008.” Asked in the 2008 Gallup poll when the effects of global warming will begin to happen, 61 percent of respondents said, “they have already begun.” The authors note that while this may only be a moderate increase from 1997, when only 48 percent gave this response, it still represents a significant shift from a large minority to a solid majority of the American public.

The main focus however is on a particularly interesting phenomenon in the United States, that of a prominent partisan split between Democrat and Republican perceptions on climate change. This split is important for two reasons. First, historically support for environmental protection in the United States has been relatively nonpartisan. After all it was Nixon who in 1970 declared the 1970s as the “environmental decade” and proceeded to pass the strongest environmental legislation the world had seen. Since 1994, however, a divide has been noticeable, especially among members of Congress. Data from the League of Conservation Voters indicate a growing divide in environmental voting records in both the House and Senate. And, “nowhere is the partisan gap on environmental issues more apparent than on climate change.” Members of Congress tend to be more ideologically polarized than the general public. Now a very large gap exists between self-identified Republicans and Democrats in terms of perceptions of global warming. Dulap and McCright further write that, “Gallup Poll results on global warming spanning a decade, including results from Gallup’s 2008 Environment Poll conducted on March 12 suggest that this skepticism among Republican and conservative elites has led rank-and-file Republicans in the electorate to follow suit.” Thus, while overall in the Untied States citizen perceptions on the seriousness of climate change have increased, “What these overall trends generally mask, however, are highly divergent trends among Republicans and Democrats.” Specifically, 11
# **III. Data**

The study is based on two‐period panel data collected by the IHDS, which was jointly carried out by researchers from the University of Maryland and the National Council of Applied Economic Research (NCAER) in New Delhi. This nationally representative survey covers a wide‐ranging set of topics, including energy use, income, expenditure, education, health, and employment. The survey covers all of India’s key states and union territories except Andaman and Nicobar Islands and Lakshadweep. The first round of the survey was carried out in 2004–05 (mostly in 2005) and collected information on 41,554 households in 33 states and union territories, 383 districts, 1,503 villages, and 971 urban blocks. The second one, conducted in 2011–12 (mostly in 2012), re‐interviewed 83 percent of the original households and split households (if located within the same village or town), and interviewed 2,134 new households, for a total of 42,152 households.<sup>4</sup> Besides collecting detailed information on income, consumption, and other household‐level welfare measures, the IHDS includes elaborate questions on household energy consumption behavior, such as fuel use, cash expenditures for fuels, time spent collecting biomass fuels, and types of stoves and electric appliances used in the household. It also asks questions related to the reliability of power supply and the source of household electricity. These detailed questions allow us to analyze the impact of electricity supply and its quality on a broad range of household economic outcomes.

The survey also covers key features of the villages where surveyed households are located. It is important to control for village‐level characteristics in the analysis because they can directly affect both the outcomes of interest (such as employment, income, and poverty status) and the probability of electricity being present in the village. While the survey was carried out in both urban and rural areas, community characteristics are available only for the rural sample. In the analysis, we therefore use only the rural sample, consisting of more than 24,000 original households.

The 2005 sample had 24,191 households. Because many of these split into multiple households over time, the number of households increased to 28,446 in 2012. Table 1 shows the distribution of sample households among six geographic regions as well as union territories as a group. North India accounts for the largest share of households in the sample (more than 30 percent), followed by South India.

Data on the rural electrification rate among sample households in 2005 and 2012 show large variation by region and year (table 2). The union territories and the rural vicinity of the national capital, New

> 4 For a detailed description of the IHDS, see the IHDS website at http://www.ihds.umd.edu.

7
<!-- Start of picture text --> () 20 40 60 80 100 120<br>FCS<br>Non FCS<br>0 2000 4000 6000 8000 10000 12000 14000<br>= GDP per capita (constant USS, bottom scale) m Trade openness(% of GDP)<br>& Natural resources rents (% of GDP) Informal economy (% of GDP)<br><!-- End of picture text --> <!-- Start of picture text --> Political Stability and<br>0.20 Voice and Accountability Violence/TerrorismAbsence of Government Effectiveness Control of Corruption<br>oo = = =<br>020<br>-0.40<br>0<br>-0.80<br>-1.00<br>-120<br>-140<br>MNon-FCS FCS<br><!-- End of picture text --> <!-- Start of picture text --> FELLaE<br>cecea<br>eseeemewenwTMs» SS<br>NPLs to Gross Loans (%) _<br>0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0<br>FCS mNon-FCS<br><!-- End of picture text --> <!-- Start of picture text --> Regulatory efficiency (O=-low to 1=high) a...<br>Tachurdon (Orlowty 3 ve PY<br>Se<br>Depth of credit information index (O=low to 0.8=high)<br>1.0 08 0.6 0.4 0.2 0.0 02 04 06 os 1.0<br>FCS mNon-FCS<br><!-- End of picture text -->
# <u>Table A17. Relationship between migration, schooling and skills in Cambodia</u>

||**A**|**ll**|**M**|**en**|**Wo**|**men**|
|---|---|---|---|---|---|---|
||(1)<br>Out of village|(2)<br>Out of village|(3)<br>Out of village|(4)<br>Out of village|(5)<br>Out of village|(6)<br>Out of village|
|Years of schooling|-0.002|-0.003|-0.015*|-0.014|0.005|0.004|
||(0.005)|(0.005)|(0.009)|(0.010)|(0.008)|(0.008)|
|Cognitive skills||0.006||-0.002||0.005|
|||(0.007)||(0.013)||(0.009)|
|Socioemotional skills||0.002||-0.006||0.004|
|||(0.005)||(0.010)||(0.009)|
|Household head can read (2011)|-0.025|-0.026|-0.075|-0.072|0.033|0.033|
||(0.032)|(0.032)|(0.052)|(0.053)|(0.053)|(0.053)|
|Household head can write (2011)|0.002|0.003|0.070|0.068|-0.056|-0.057|
||(0.030)|(0.030)|(0.052)|(0.052)|(0.053)|(0.053)|
|HH SES in 2008|-0.008|-0.008|0.000|-0.000|-0.014|-0.014|
||(0.009)|(0.009)|(0.016)|(0.016)|(0.012)|(0.012)|
|Respondent is female|0.001|0.002|||||
||(0.012)|(0.012)|||||
|Constant|0.124***|0.138***|0.162**|0.136|0.199***|0.211***|
||(0.035)|(0.040)|(0.073)|(0.091)|(0.053)|(0.055)|
|Observations|2,688|2,688|1,249|1,249|1,439|1,439|
|Adjusted R-squared|0.066|0.066|0.084|0.083|0.055|0.054|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Age FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sample|All|All|All|All|All|All|
|Number of clusters|378|378|309|309|324|324|
|Mean dependent|0.0763|0.0763|0.0817|0.0817|0.0716|0.0716|

* p<.10  ** p<.05  *** p<.01

_Notes_ . The dependent variable “out of village” is a dummy variable that indicates whether the respondent lives outside the village where we originally surveyed their household. The cognitive skills index is the Mathematics score, computed using Item Response Theory with a two-parameter logistic (2PL) model. The items included in the model are those from the tablet-based computer-adaptive test. The socioemotional skills index is computed using principal-component factor analysis on 15 Big Five items and 8 Grit items. The sample for this long-term follow-up is 3,294 respondents. We only have information on where the respondent currently lives for 2,706 respondents (1259 men and 1,447 women). Among them, some respondents did not complete the tablet-based computer-adaptive test or answered “Don’t know” to the socioemotional skills questions, leading to a sample of 2,688 respondents for regressions that include these skills measures (1,249 men and 1,439 women). All regressions control for village and age fixed effects. Standard errors are in parentheses (clustered at the village level).

101
FIGURE 4

# HUNGARY: REAL EXCHANGE RATE (Simulated values under altemative stabilization strategies)

<!-- Start of picture text --> 105<br>1005- Simulation 4<br>95<br>90 - Simulation 2<br>90mulati I<br>85<br>so ~~~~~~~~~~~~~~~~~~~Simulation 3<br>75<br>1991101 1991.04 1991.07 1991.10 1992.01 1992.04 1992.07 1992.10l<br><!-- End of picture text --> 31
minute local call, which was about US$0.08, was about 71 percent of the Sub-Saharan<sup>African</sup> average (see Figure 5) in 1996. However, since tariffs were set in nominal terms<sup>inlocal</sup> currency, prices fell rapidly over the next few years due to high inflation (see Table<sup>1).By the</sup> end of 1998, the cost of a three-minute call had fallen to about US$0.03, far lower<sup>thanthe</sup> average for Sub-Saharan Africa (US$0.10). In 1996, when the tariffs were originally<sup>set, athree-</sup> minute long-distance call (320 KM) cost 9 Kwacha (US$0.59 in 1996). Since long-distance tariffs were also set in nominal terms in local currency, they also declined over time in real terms, reaching US$0.21 for a three-minute long-distance call by 1998. The cost of long-distance calls in both monetary terms and relative to the cost of local calls was low by regional standards (see Table 2).

In contrast, international call rates were very high, even by regional standards (see Table 2). In 1996, a three-minute call to the United States or Europe cost US$9.00 (see Table 3). This was also especially high when compared to local call rates - a threeminute call to the United States from Malawi cost 160 times more than a three-minute local call. In comparison, the average was about 60 times more for SSA (see Table 2). Another notable difference between local and international calls was that the 1996 Consequently, international rates <!-- Start of picture text --> 12<br>10<br>8_<br>6 s. ~<br>D 4<br>2- o<br>1896 1997 1998<br>-|-- Malawi -0- Sub-Saharan Africa (Average)<br><!-- End of picture text -->

Figure 5: Cost of a Three-Minute Local Call in Malawi and Other Sub-Saharan Countries (US¢Z, End of Year). **Note: Tariffs and exchange rates are for end of year. Source: Data for Sub-Saharan Africa is from International** Telecommunication Act set international rates in dollars rather than Kwacha. remained high in real terms before reform, despite high inflation.

> **8** According to International Telecommunication Union (1998b), the largest cities in Malawi had 6.18 main lines per 100 inhabitants in 1998 while the rest of the country had 0.05 main lines. The average for low-income countries at this time was 5.67 main lines per 100 inhabitants in the largest cities and 1.09 main lines elsewhere.

12