rafmacalaba/datause-encoder-data huggingface dataset:
# **Ladies First?**

## **Firm-level Evidence on the Labor Impacts of the East Asian Crisis**

Mary Hallward-Driemeier, World Bank<sup>1</sup> Bob Rijkers, World Bank Andrew Waxman, Cornell University

**Key words:** Financial crisis, discrimination, women, job flows, manufacturing **JEL Codes: D22, E32, J31, J71, J63**

1 This work was supported by the Gender Action Plan of the World Bank (TF 070751). We would like to thank Ana Fernandes, Fitria Fitrani, Leo Iacovone, Kai Kaiser, Alex Korns, David Newhouse, Rifa Rufiadi and William Wallace for their assistance in obtaining the data and in discussing the policy environment. We would also like to thank Rabia Ali, Elena Bardasi, Eric Bartelsman, Asli Demirguc-Kunt, John Giles, William Maloney, David Newhouse, Josefina Posadas, Rita Ramalho, Ana Revenga, Luis Serven, Dominique van de Walle, and seminar participants at the World Bank and the IZA Conference on Employment in Development for their comments. The views expressed here are those of the authors and do not necessarily represent the views of the World Bank, its Executive Board or member countries. The corresponding author is Mary Hallward-Driemeier, 1818 H Street, NW, Washington DC, 20433; mhallward@worldbank.org; tel: 202-473-9120; fax: 202-522-1155.
weaknesses as well as its surprisingly strong performance relative to much less parsimonious approaches.

Based on this model, we estimate the housing demand equations for owners and renters in the four cities, reported in first two equations in Table 3, i.e., equations 3.1 and 3.2. We also report the results from two other equations in the table, i.e., equations 3.3 and 3.4. These latter equations are reduced form estimates that also take supply factors in each city into account by including dummy variables for the other cities.<sup>15</sup> As our earlier discussion indicated, we expect the policy regimes in the other cities to be relatively more enabling than Accra’s. Consequently, we expect the signs of the other city dummy variables—what might be termed supply shifting variables—to have a negative relationship with our dependent variable, rents.<sup>16</sup> In the end, our model is clearly very simple, but nevertheless, as described in Malpezzi and Mayo (1987), it is firmly rooted in traditional estimates.

**Table 3. Explaining Housing Rents and Prices**

|**Depe**<br>**expe**|**ndent varia**<br>**nditure on h**|**ble: Log of hous**<br>**ousing and serv**|**ehold**<br>**ices**|**Intercept**|**Log of**<br>**income**<br>**per**<br>**capita**|**HH**<br>**size**|**HH size**<br>**square**|**Dummy**<br>**variable**<br>**Addis**<br>**Ababa**|**Dummy**<br>**variable**<br>**Nairobi**|**Dummy**<br>**variable**<br>**Dar es**<br>**Salaam**|
|---|---|---|---|---|---|---|---|---|---|---|
|3.1|Owners|N=49<br>Adjusted R<br>square=0.36|_Coefficient_<br>_T statistic_|3.04<br>5.72|0.53<br>4.97|-0.07<br>-0.62|0.00<br>0.46||||
|3.2|Renters|N=28<br>Adjusted R<br>square=0.63|_Coefficient_<br>_T statistic_|1.04<br>1.85|0.72<br>6.14|0.24<br>1.93|-0.01<br>-1.34||||
|33|Onr|N=49<br>Adtd R|_Coefficient_|3.05|0.65|0.03|-0.01|-1.09|-1.12|-1.21|
|.|wes|juse<br>square=0.74|_T statistic_|8.29|7.85|0.36|-0.65|-3.62|-5.18|-7.10|
|3.4|Renters|N=28<br>Adjusted R|_Coefficient_|2.21|0.63|0.33|-0.02|-1.14|-1.37|-1.41|

15 See Annex 1 for definitions, descriptions, and summary statistics for all the variables in the table. 16 We use the same definitions for rents and net imputed rents as do Malpezzi and Mayo (1987), i.e. household expenditure on housing and services.

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of its capital would be determined by its expected objectives and by the cash flow needs of its expected operations. In the case of a Fund, its minimum size would be such that the management fees (usually 1.5 to 2.5 percent of the Fund's paid-in-capital or of its net assets) would be enough to finance the day-to-day operations of the management company. The management team is usually structured around a small team (two to three people) of highly qualified staff, whose cost varies substantially from country to country. The greater the experience of the general partner, the more capital it will be able to raise from investors.

While every venture capita-ist develops its own investment policies and procedures, it is possible to discern a great many similarities among them and to group the procedures under equivalent functions: i) deal generation; ii) due diligence process: iii) structuring of the deal (terms and conditions); iv) monitoring and adding value to the investment; v) portfolio management; vi) selling and liquidifying. Each of the above functions requires special skill, appropriate timing and its own procedures.

Successful venture capital investment depends heavily on generatina a good "deal flow" from which to choose. Venture capital operators in the United States report that the best deals come from referrals of other entrepreneurs, from other venture capitalists seeking syndicate partners and from other business people such as: accountants, lawyers and financial consultants. Surveys in France have shown a similar pattern, highlighting the role of the banking community as referral source to the venture capitalists. Nevertheless, active marketing should not be disregarded and would usually include: direct mailing, newsletters, seminars, and workshops. The regular flow of projects is important because, as a rule of thumb derived of expost data of companies, in many countries only ten to twenty out of 100 inquiries, would survive the initial screening and would reach the due diligence stage, and only two or three of them are likely to lead to an investment.

The due diligence process is the careful review of the business plans, which arz submitted by entrepreneurs in all different forms, sizes and circumstances. At the front end of the due diligence process is a screening mechanism for turning down deals that do not meet the overall venture capitalist's predetermined criteria. An experienced venture investor should be prepared to say "no" quickly if he wants to have enough time (four to ten weeks) to review the good proposals. The due diligence process, which is neither short nor sweet, would focus upon those areas which the venture capitalists has, through practice, identified as critical for the success or failure of most companies: i) quality of the entrepreneur; ii) depth of the
# Appedix 1: Data sources and description

The data used in this study are compiled from (1) DHS Final Reports (2) NFHS Final Reports and (3) Reconstructed gender arLd provincial disaggregated outcomes for Pakistan. Except for a very few exceptions, the transfonnati ons required to go from the published data to that used here involve no more than taking the ratio of the female to the male value. The exceptions are discussed below.

# _Baluchistan province of Pakistan_

Because of the limited data from the Baluchistan province of Pakistan, the data were adjusted as follows. The female/male child mortality ratio (4q1) is not that derived from the DHS but is calculated from the Pakistan Integrated Household Survey (PIHS) which was carried out in 1991. The ratio as calculated from the DHS data is 8.92 (4q1 for males is 6.4, for females it is 57.1, which seems implausible). The ratio calculated from the PIHS is equal to 1.79. In the calculation of consultation and no treatment the ratios are again implausible when including all children who suffered from diarrhea, ARI, and fever. These ratio are replaced by the ratio including only those who had a sample weight of less than 1. The corresponding changes in the data are as follow:

## Changes made to Baluchistan female / male ratios for 6 variables

|Description|Name|Raw ratio|Ratio using only<br>those observations<br>with weight less<br>than I|
|---|---|---|---|
|Female relative to male: percent with ARI who were<br>taken for consultation (usually includes hospital, health<br>center, clinic, doctor, or other health professional)|aritd|.713|1.00|
|Female relative to male: percent wilh fever who were<br>taken for consultation|fevtd|.519|.984|
|Female relative to male: percent with diarrhea who<br>were taken for consultation|diatd|2.82|.271|
|Female relative to male: percent wilh ARI who<br>received no treatment|arind|9.09|2.60|
|Female relative to male percent wilh fever who<br>received no treatment|fevnd|2.32|1.42|
|Female relative to male percent wilh diarrhea who<br>received no treatment|diand|5.09|3.26|

# _Consultation and no treatment of Fever or ARI_

Conditional on a child having suffered from fever or ARI respondents were asked whether the child was taken to a health facility or provider for a consultation. However, not all countries have this number for both fever and ARI. In order to create and "index" of the female/male ratio of consultation for fever or ARI, the data is "filled in" by predicting the ratio for ARI (from a regression of ARI on fever) and using the predicted value when the actual value for ARI is missing and that for fever is not, 30
Policy Research Working Paper 9427

# **Abstract**

This paper develops and estimates a model of supply and demand for child care. On the demand side, one- and two-parent households make consumption, labor supply, and childcare decisions. On the supply side, non-parental care providers include child care centers, paid caregivers who operate at the child’s home, and unpaid, informal care-givers. Centers make entry, price, and quality decisions under monopolistic competition. Child development is a function of the time spent with each parent and non-parental care providers; these inputs vary in impact. The model’s structural parameters are estimated using the 2003 Early Childhood Longitudinal Study, which contains information on parental employment and wages, child care choices, child development, and center quality. The model features locations that differ in size and median family income. Parameter estimates are used to evaluate multiple policies including vouchers, cash transfers, and public provision. Vouchers that can only be used in high-quality centers or that require mothers to work are particularly effective, as - they deliver child development gains while increasing moth ers’ labor supply, thereby limiting policies’ fiscal cost.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mferreyra@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_Source_ : LFS, SILC, imputed OLS, regionally deflated, authors’’ calculations

# Table 19. Robustness Checking

<!-- Start of picture text --> 2009 2013 2009 2013 2009 2013<br>Model Probit OLS OLS<br>Specification Mean Time Invariant – Not Regionally Deflated Time Invariant – Regionally Deflated<br>NSB - Host community 45.98 42.76 45.86 41.89 44.16 40.30<br>NSB - Recent migrants 17.81 49.01 16.44 47.82 15.63 46.70<br>NSB -Settled migrants 28.33 19.13 28.25 18.60 27.50 17.74<br>Rest - Host community 20.17 17.37 20.01 17.08 21.06 17.90<br>Rest - Recent migrants 6.06 13.23 5.88 13.23 7.30 16.13<br>Rest - Settled migrants 8.37 5.98 8.19 5.83 9.14 6.56<br>Figure 8. Robustness Checking<br>Near Syrian Border - Near Syrian Border - Near Syrian Border - Rest of the country - Rest of the country - Rest of the country -<br>Turkey<br>Host community Recent migrants Settled migrants Host community Recent migrants Settled migrants<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Type<br>Probit - Not Regionally Deflated<br>OLS - Not Regionally Deflated<br>OLS - Regionally Deflated<br>Poverty Rate ($5 PPP, %)<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br><!-- End of picture text --> 36
positive effect of school size and no effect of class size. The current paper extends that analysis by drawing on a much bigger sample of thousands of Ukrainian schools. The size of the current dataset not only provides for more precise estimates but also allows for the analysis of more precise questions, like an analysis by gender or by type of settlement. In addition, the current dataset allows us to analyze the Ukrainian plan to close schools with less than 100 students.

# **IV. Data and Analysis**

In this paper, we use several datasets which were matched and combined into one comprehensive dataset. From the Ministry of Education, we obtained data, for all Ukrainian schools, on the total number of students, the number of students by grade, the number of classes by grade and the total number of teachers and staff.

Data about performance on the EIT were obtained from the yearly public data files, posted on the EIT site, which provide, for each school, the distribution of students‟ scores over 10 intervals. In addition, from the Ukrainian Centre for Education Quality Monitoring we obtained mean and median scores data and information about the gender and the language choice of students<sup>22</sup> .

A more detailed description of these sources and the matching process can be found in the appendix A1.

## _a. Descriptive Statistics for 2010_<sup>_23_</sup>

For 11683 Ukrainian schools we have information on both 2010 EIT test scores (from the EIT database) and information on input variables (from the Ministry of Education).

Given that our main variables of interest, school size and class size, but also other input variables are very different depending on whether or not the school is located in an urban or rural area, we provide the descriptive statistics for rural and urban schools separately.

> 22 The correspondence between those two sources of data was high but not perfect. For a few schools, we had no mean scores and for somewhat more schools the total number of students according to the two databases was not equal, though such deviations were typically very small. One possible explanation for these differences is that the database of the Ministry‟s EIT Center is updated, f.e. correcting mistakes or including the results of appeals. 23 The descriptive statistics for 2008 and 2009 give a qualitatively similar picture. Note that in this section we will only look at differences in means between groups, delaying the question of significance to the regression analysis.

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# Tax Evasion and the Contribution-Benefit Link: The Case of Maternity Benefits<sup>∗</sup>

Anik ́o B ́ır ́o P ́eter Elek D ́aniel Prinz L ́aszl ́o S ́andor

**Keywords:** contribution-benefit link, social security, tax evasion, parental benefits **JEL codes:** H26, H55, J46

> ∗B ́ır ́o: HUN-REN Centre for Economic and Regional Studies (biro.aniko@krtk.hun-ren.hu). Elek: HUNREN Centre for Economic and Regional Studies and Corvinus University of Budapest (elek.peter@krtk.hunren.hu). Prinz: World Bank (dprinz@worldbank.org). S ́andor: Consumer Financial Protection Bureau (laszlo.sandor@cfpb.gov). B ́ır ́o was supported by the National Research, Development and Innovation Office of Hungary (grant number: OTKA K 146309). We thank Pierre Bachas, Anne Brockmeyer, Shoumeli Das, Sandra Gain, Nicolas Gavoille, D ́aniel Horn, Yunan Ji, J ́anos K ̈oll ̋o, Attila Lindner, Oyebola Okunogbe, Rita Pet ̋o, Bal ́azs Reizer, Mahvish Shaukat, Andr ́as Svraka, Annalisa Tassi, Roula Yazigi, Anna Zasova, Roman Zhukovskyi, and seminar participants at the Centre for Economic and Regional Studies, the CERS Summer Workshop, the Hungarian Society of Economics Annual Conference, the Shadow2023 Tallinn conference, the Hungarian Labor Market Conference, the World Bank, the Helsinki Graduate School of Economics, and the University of Liverpool for helpful comments. The administrative database used in this paper is owned by the National Health Insurance Fund Administration, the Central Administration of National Pension Insurance, the National Tax and Customs Administration, the National Employment Service, and the Educational Authority of Hungary. The data was processed by the Databank of the HUN-REN Centre for Economic and Regional Studies. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not represent the views of the World Bank and its affiliated organizations or those of the Executive Directors of the World Bank or the governments they represent. This article is the result of L ́aszl ́o S ́andor’s independent research and does not necessarily represent the views of the Consumer Financial Protection Bureau or the United States. Declarations of interest: none.
The model indicates that, for every kilometer of elevation, the temperature drops 2.15oC, and for every latitudinal degree further south, the temperature drops 0.39oC. This information was used to estimate temperature in all the remaining districts, using the altitude and latitude of the district capital.

Rainfall does not present such simple regularities, so in order to estimate rainfall for the districts where this information was missing, we used the 10-minute latitude/longitude data set of mean monthly surface climate over global land areas, constructed by New et al. (2002). This data set includes precipitation data and was interpolated from a data set of station means for the period centered on 1961 to 1990.<sup>3</sup> In order to assess the climate change trends in the different parts of Peru, we obtained monthly temperature and rainfall data from 1948 to 2008 from the Monthly Climatic Data for the world (MCDW) publication of the US National Climatic Data Center (NCDC). The constructed data set was complemented with data from the Global Climate Observing System of the NCDC (sent by request) and data obtained from the National Meteorological and Hydrological Service of Peru (SENAMHI). The data are described in more detail in Section 4 below.

# **3. Modeling climate and human development**

In this section, we estimate the contemporary relationship between climate and human development in Peru. Two dimensions of human development are analyzed: income and health, because these are the ones that most directly could be affected by climate change. Education, on the other hand, is treated as an explanatory variable instead of a dependent variable.

As several researchers have pointed out, the relationship between temperature and development is likely to be hump-shaped, as both too cold and too hot climates may be detrimental for human development (Mendelsohn, Nordhaus & Shaw, 1994; Quiggin & Horowitz, 1999; Masters & McMillan, 2001, Tol, 2005). In order to allow for this possibility we include both average annual temperature and its square in the regression. The same argument also holds for precipitation and possibly also urbanization rates, which is why we also include precipitation and urbanization rates squared.

Thus, the regressions in this section will take the following form: where _yi_ is a measure of the income level in district _i_ , _tempi_ and _raini_ are normal average annual temperature and normal accumulated annual precipitation in district _i_ , _edui_ is a

> 3 The data are available online at the School of Geography at Oxford  (http://www.geog.ox.ac.uk), the International Water Management Institute "World Water and Climate Atlas" (http://www.iwmi.org) and the Climatic Research Unit (http://www.cru.uea.ac.uk).

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with higher levels of schooling infrastructure to have larger migrant networks, which would bias us towards finding that positive selection on education occurs more in high migration communities.

To account for this concern we follow Woodruff and Zenteno (2007) and a number of subsequent studies in using historic state-level migration rates as an instrument for current migration networks.<sup>9</sup> In particular, we use the U.S. migration rate from 1924 for the state in which the migrant household is located, taken from Foerster (1925). Likewise, we will use the interaction between education and historic migration networks as an instrument for the interaction between education and community migration prevalence. The main argument used to justify the use of this instrument is that these historic migration rates were the result of the pattern of the arrival of the railroad system in Mexico, coupled with changes in U.S. demand conditions for agricultural labor. As migration networks lower the cost of migration for future migrants, they then become self-perpetuating.<sup>10</sup> These instruments are strong predictors of current migration prevalence and its interaction with education. Appendix table A1 shows first-stage F-statistics between 40.5 and 54.1. To justify the exclusion restriction, we need to argue that these historic rates affect current migration decisions only through current migration networks. A potential threat to this instrument is that communities which responded more to the expansion of the railroad may have been ones with historically poor schooling infrastructure and inequality, or that the development of the railroads ushered in the expansion of infrastructure such as schooling facilities. This could affect current education through the intergenerational transmission of schooling, and through the inertia in schooling infrastructure. To allow for this possibility, we control for a number of historic variables that capture schooling access, achievement, and equality, and for historic measures of inequality. Even after controlling for these variables, the historic migration rates remain strong predictors of current community migration prevalence.

> 9 Hanson and Woodruff (2003); McKenzie and Rapoport (2006a, 2006b); López-Córdoba (2005); and Hildebrandt and McKenzie (2005) all employ historic migration rates as instruments for current migration.

> 10 Hildebrandt and McKenzie (2005) and McKenzie and Rapoport (2006a) provide more detailed discussion of the historic processes involved .

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