rafmacalaba/datause-encoder-data huggingface dataset:
evaluation study consisted of a group of program participants and a control group that were selected on the basis of 2002 administrative data on training program clients.

In Romania, eligibility for ALMP programs depended on being registered unemployed, having income less than 50% of the minimum wage, being unemployed due to layoffs, having been employed at least 6 months of the last 12 months or being a recent graduate from school or university. There were various types of training available to program participants, including a public service component, whereby local government and other eligible organizations could propose public projects with a maximum cost of up to $50,000 and hire ALMP participants to work on them. The analysis sample was comprised of individuals who entered the register not earlier than January 2001 and got off no later than December 2002.

Benus et al. (2005) evaluate the impacts of both the Russian and Romanian ALMP programs overall and separately by gender, using a propensity score methodology. The propensity score model is based on a fairly limited set of predictors of program participation, that include gender, age and education. Nonparticipants are people who applied for training but were not selected for it, so they would be expected to differ in some respects from participants. The outcomes of interest in the evaluation are the likelihood of being employed at the time of the follow-up survey, the likelihood of being employed at least once after the program, the likelihood of having a high salary, and the length of the current unemployment spell. The impact evaluation finds no significant effects of the ALMP programs in Russia, on the whole. In Romania, however, the program is found to have had a statistically significant impact for three of the four outcomes (the likelihood of employment, the likelihood of being employed at least once, and the level of wages were all higher among participants in Romania). Subgroup analyses reveal some gender, age, and education heterogeneity in the impacts for Romania. Re-training increased the probability of employment and decreased the wage for females. That is, re-training appears to have helped find employment but at a lower wage than the individuals would have found on their own. Middle age and lower education level individuals experienced the biggest program impacts. For men, the re-training was found to have had no effect. The authors hypothesize that the difference in impact findings between Russia and Romania may be attributed to differences in the characteristics of program participants affecting their labor market prospects; the Russian sample was better educated (45% had a university degree), older and had more labor force experience than the Romanian sample. In both countries, the program was not found to have been beneficial 20
# **–** **<u>Table 6 Antidumping Measures as Notified to WTO, 1995 June 2004</u>**

|Measures|1995|1996|1997|1998|1999|2000|2001|2002|2003|Jan.–<br>June<br>2004|Totals|Per<br>year<br>1995–<br>J2004|Per<br>year<br>1995–<br>2001|Per<br>year<br>2002–<br>J2004|Measures<br>in place as<br>of June<br>30, 2004|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Argentina|13|20|11|12|9|15|15|24|19|1|**139**|14.6|13.6|17.6|76|
|Brazil|2|6|2|14|5|9|13|4|2|2|**59**|6.2|7.3|3.2|54|
|Chile|2|0|2|2|0|0|0|0|0|0|**6**|0.6|0.9|0.0|0|
|Colombia|1|1|1|0|6|2|0|0|0|0|**11**|1.2|1.6|0.0|0|
|Costa Rica|0|0|0|0|0|0|0|0|1|0|**1**|0.1|0.0|0.4|1|
|Mexico|16|4|7|7|7|7|3|4|7|3|**65**|6.8|7.3|5.6|58|
|Peru|2|1|2|0|3|4|1|6|7|4|**30**|3.2|1.9|6.8|29|
|Major users, for<br>comparis<br>on||||||||||||||||
|United States|33|11|20|16|24|32|33|24|12|6|**211**|22.2|24.1|16.8|293|
|European<br>Community|15|23|23|26|18|41|13|25|3|6|**193**|20.3|22.7|13.6|165|
|South Africa|0|8|18|14|35|13|5|15|1|2|**111**|11.7|13.3|7.2|84|
|India|7|2|8|22|22|57|38|64|53|6|**279**|29.4|22.3|49.2|216|

_Source:_ WTO **Database** 49
source of omitted variable bias. Further, following the strategy of Rajan and Zingales (1998) for guarding against endogeneity concerns, we test for some theoretical predictions about how credit supply affects the choice of woman vs. man top manager. Fourth, we focus on developing countries, which is a novelty in the literature (see Section 2). Fifth, we use firm-level survey data which allows us to account for several potential omitted variables such as firm size and productivity, industry, and gender composition of workers and firm owners.

# **2. Conceptual framework and literature review**

# 2.1 _Gender gap in top management_

Gender gaps favoring men over women exist in most countries and in diverse areas like labor market outcomes, health, education, and political empowerment (see Klasen 2020, World Bank 2011). Likewise, the share of women among top managers of firms is also low when compared to their share in total population and in lower-level jobs. See Fernandez and Campero (2017), European Union (2016), Coric (2018), Peng and She (2020), Islam and Amin (2016), Islam et al. (2019), and Sekkat et al. (2015). For example, Coric (2018) for 45 countries in Europe and Islam and Amin (2016) for 73 developing countries report that only 26 percent and 19 percent of the top management positions, respectively, are held by women.

Direct evidence on the drivers of the gender gap in top management positions is limited. However, the broader literature on gender gaps offers several potential explanations. First, studies report that compared to men, women are more risk averse (see Croson and Gneezy 2009, Dohmen et al. 2011), and prefer less competitive environments (see Niederle and Vesterlund 2007, Gneezy et al. 2003) and more flexible work conditions (see Goldin 2014, Bertrand 2018). Thus, women may shy away from managerial work that is more risky, competitive, and requires a long and 3
|**Country &**<br>**Region**|**World Bank**<br>**Income**<br>**Group (2020)**|**Author(s)**|**Study**<br>**Period**|**Methodology,**<br>**including**<br>**identification**<br>**strategy**|**Multiplier range**|**Type**|
|---|---|---|---|---|---|---|
|**102**<br>**Developing**<br>**Countries**||Kraay<br>(2014)|1970-2010|IV to address<br>endogeneity (the lags<br>between<br>commitments and<br>eventual<br>disbursements on<br>loans by official<br>creditors to<br>developing country<br>governments)|0.4|Spending|
|11 European<br>Countries|High Income|Deleidi_et_<br>_al._(2020)|1970-2016|Local Projections<br>approach on a panel<br>dataset and<br>considering different<br>model specifications|0.96|Public Investment|
|17 OECD<br>economies|High Income|Abiad_et al._<br>(2015)|1985-2013|Local projections<br>method (estimates<br>impulse response<br>functions by directly<br>projecting a variable<br>of interest on shocks<br>as well as lags of<br>variables usually<br>entering a vector<br>autoregression)|1.4 (medium-term)|Public Investment|

_Source:_ Compiled by authors As shown by the table, fiscal multiplier estimates (even using the same broad methodology, country, and time period) are heterogeneous. Some reasons for the differences across estimates are due to the role of institutional settings or the asymmetry of fiscal multipliers in different business cycle phases. Data choices and identification strategies are also found to have important effects on the precision of multiplier estimates.

Overall estimates for public spending and investment multipliers are modest and generally less than one. However, what is most striking about the findings is that public investment multipliers grow and have more lasting effects over time. Studies estimating the impact for the long run or over a multi-year horizon find larger multipliers than the studies measuring the impact on the same year or after one year. As illustrated by Leeper _et al._ (2010) implementation delays can produce small or even negative labor and output responses in the short run.

In sum, the key lessons underscore that the spending multiplier is highly sensitive to different modeling and methodological choices, as well as even more innocuous choices of time period considered in the analysis. Overall, one of the strongest findings is that investment and spending are more effective than tax cuts and transfer payments. In the next section, we will consider whether such a finding will be robust to the different specifications of the “state” of the economy and other institutional factors.

7
11 especially high in those countries where unequal land distribution severely limit incomeearning opportunities in agriculture.

The Gini coefficient of percapita income for the sample is: 0.532. On the one hand, this Gini is quite a bit higher than the Gini (0.321) which can be calculated from per capita expenditure data for the sample. On the other hand, since income data include savings, it is natural to expect that the Gini coefficient of income will be higher than that for expenditure. Moreover, the Gini coefficient of income from this study seems well within the range of income Ginis recorded for other developing countries. For instance, the income Ginis recorded in the most recent edition of World Development Indicators (1998: Table 2.8) suggest that Gini coefficients of per capita household income range from a low of 0.420 (Bolivia) to a high of 0.601 (Brazil).<sup>9</sup> In Table 2 the five sources of income in rural Egypt are presented by income quintile group. The results demonstrate the importance of nonfarm income for the poor. The poor - that is, those in the lowest quintile group - receive almost 60 percent of their mean total per capita income from nonfarm income. This figure is 65 percent higher than that received by the poor from agricultural income, and more than ten times that received by the poor from transfer, livestock or rental income.'10 Evidently, the very real land constraints in rural Egypt - 75.7 percent of the households in the sample own no land" force the poor to seek the bulk of their livelihood from outside agriculture.

Table 3 presents another way of showing the dependence of the poor on nonfarm income. In this table households are ranked by size of land owned. Like other studies,'<sup>2</sup> the data reveal an inverse relationship between size of land owned and the share of nonfarm income. For the poorest (that is, landless group), non-farm income accounts
In each user country-specific section that follows below, we add basic information on each country’s antidumping process. We also describe key missing elements of the data that we experienced during the data collection. Finally, so as not to throw away useful, but country-specific information, for each country’s spreadsheets we describe the additional data columns in AD-CTY-Master, AD-CTYProducts, AD-CTY-Domestic-Firms, AD-CTY-Foreign-Firms spreadsheets beyond the columns of data described in section 2.

# **3.1 Argentina (ARG)**

Argentina uses a dual track system: dumping is assessed by the Secretariat for Industry and Commerce while injury is determined by the National Commission for Foreign Trade (CNCE). Antidumping decisions by the two agencies are contained in Acts (Actas) and Resolutions (Resoluciones), which are available through the Economic Ministry’s “Documentation and Information Centre.” The source of the underlying data was online publications of Acts and Resolutions via the website of the Economic Ministry’s Documentation and Information Centre as well as tables in Nogués and Baracat (2005), which describes more detail on the Argentine antidumping process.

The Argentine spreadsheet contains additional data beyond the standard information described in Section 2. The additional items are described in the table below.

15
- Padron Historico de Beneficiarios de Plan NACER _—_ the historical record of Plan Nacer beneficiary status at the individual level.

- REMEDIAR _—_ individual pharmaceutical records linking an individual’s national identify number (Documento Nacional de Identidad — DNI) to the clinic that prescribed/dispensed the medicine.

- Plan Nacer clinic billing and payment administrative records.

- 2001 Population Census — proportion of eligible mothers (without formal health coverage) in the clinic's locality.

- SISA — Sistema Integrado de Información Sanitaria Argentino _—_ the geographic coordinates for those health facilities with complete information.

The prenatal care and birth outcome indicators that are used in the analysis were obtained from SIP birth and medical record files kept at maternity hospitals.<sup>8</sup> Each hospital collects these data in its registries for every birth on an ongoing basis. About half of these records are automated in the SIP, while the rest are on paper. Plan Nacer automated the paper records, and we merged them with records already in the SIP data to obtain a dataset comprising the universe of births in maternity hospitals from 2004 to 2008 in the seven northern provinces.

# **3.2. Analysis Sample**

Our starting point was the universe of births in maternity hospitals. We were able to automate or obtain automated birth records for 78 percent of births in the study period. We then determined whether the mother was a beneficiary of Plan Nacer and the clinic where she obtained her prenatal care. Using the mother’s national identity number (DNI) and address we were able to match our existing dataset (described above) to the other administrative databases. Specifically, we constructed the sample used for analysis as follows:

1. _Identify those mothers that became beneficiaries before giving birth._ We used the mother’s DNI to check the Plan Nacer beneficiary roster to be able to determine her beneficiary status at the time of pregnancy. A mother was designated to be a beneficiary if she had enrolled in Plan Nacer prior to giving birth, and the clinic that she attended for prenatal care was also enrolled in the Plan at the same time.

2. _Identify the clinic used by the mother for prenatal care._ Birth records were compiled from the hospital where the mother gave birth. However, the clinic

> 8 Hospital medical staff collects birth record data, and birth outcome data are not directly incentivized at the provider level. These data are equivalent to birth record data collected in the United States. Thus, any misreporting on a system wide basis on behalf of provinces would involve massive collusion between service providers and the provinces.

14
|_PEXPp_,_t_|Public service expenditures in sector_p_|
|---|---|
|_PUBINVt_|Total public investment|
|_RECCOSTp_,_t_|Recurrent public service expenditures in sector|
|_ROADCOSTt _|Total road investment and maintenance cost|
|_ROADINVt _|Total road investment cost across regions|
|_ROADWAGEi_,_t_|Road building wage across regions|
|_RWGOVRi_,_t_<br>_RWGOVUt _|Real wage in rural government net of food<br>Real wage in urban government net of food|
|_RWMANt_|Real wage manufacturing sector net of food|
|_SAVGNPWFt_|Total saving as a share of GNP with food|
|_SAVRi_,_t_|Rural gross saving in region|
|_SAVTOTt_|Total gross saving|
|_SAVUt_|Urban gross saving|
|_TAXt_|Total tax revenues|
|_TBt_|Trade balance|
|_TOTEXPt_|Total expenditures across sectors in each period|
|_TOTIMPGt _|Total government public service sector imports|
|_YDISt_|Disposable income (total population)|
|_YGOVRi_,_t_|Real rural public sector wage net of food|

53
by Acemoglu and Zilibotti (1999).

On a related point, in our model countries will differ only in their levels of wealth, and wealth levels will determine the direction of financial comparative advantage. Differences in institutional quality, such as enforcement of contracts and property rights, are clearly important in driving the pattern of financial comparative advantage. Adding institutional differences to our framework will leave the main conclusions unchanged, and in fact reinforce the strength of financial comparative advantage as long as wealthier countries are also the ones with better institutions.

It is important to note that the effect of trade opening on financial development we illustrate here is one of many that could be relevant in practice. For instance, trade can increase uncertainty and income variability of agents within the economy (Newbery and Stiglitz, 1984). Financial system could then be expected to grow after trade opening, as agents’ demand for insurance increases.<sup>1</sup> This type of mechanism is not inconsistent with the effect we are proposing. One important difference, however, is that our mechanism affects countries differentially, while the alternative one unambiguously implies an improvement of the financial system in all countries.

The model predicts that in wealthy countries, trade should be associated with faster financial development. By contrast, in poor countries, more trade should lead to slower financial development, as these countries import financially intensive goods rather than develop their own financial system. We use data on financial development for a sample of 77 countries compiled by Beck, Demirguc-Kunt, and Levine (2000) to show that the pattern predicted by the model seems to find empirical support. While for developed countries, higher trade openness is associated with faster growth of the financial system, developing countries that traded more experienced slower growth in their financial systems.

The rest of the paper is organized as follows. Section 2 presents the basic model and shows that when the amount of lending and the quality of the financial system are equilibrium outcomes, they will be influenced by trade. Section 3 discusses empirical evidence. Section 4 concludes.

> 1 Rodrik (1998) shows that more open countries have larger governments, which helps them deal with increased uncertainty that is associated with openness. Svaleryd and Vlachos (2002) provide empirical evidence that countries with better developed financial systems are more likely to be open to trade, and argue this is because a better financial system allows a country to better cope with increased uncertainty. Tangentially, these authors also provide some evidence that the financial system improves after trade opening.

4
57

# Table 10 Panel results: SUR estimation

Dependent Variable: GDITOY? Method: Seemingly Unrelated Regression Sample: 1980 1999 Included observations: 20 Number of cross-sections used: 5 Total panel (unbalanced) observations: 91 One-step weighting matrix

|Variable|Coefficient|Std. Error|t-Statistic|Prob.|
|---|---|---|---|---|
|C|20.735|1.758|11.795|0.000|
|FDITOYHATP|0.500|<br>0.275|1.817|0.073|
|GOVYCHATP|-0.099|0.034|-2.908|0.005|
|GOVYKHATP|0.454|0.110|4.124|0.000|
|LGPRLCRHATP|0.923|0.361|2.559|0.012|
|VOL12DLGTRLCRHAT|P<br>-10.133|<br>4.585|-2.210|0.030|
|GROHATP(-1)|0.364|_0.155_|2.348|0.021|
|<br>STATEFAILAVG|-12.071|1.861|-6.487|0.000|
|Weighted Statistics|||||
|Unweighted Statistics|||||
|R-squared|0.393|Mean depe|ndent var|20.69|
|<br>Adjusted R-squared|0.341|<br>S.D.depen|<br>dent var|3.57|
|<br>S.E. of regression|2.893|<br>Sum square|<br>d resid|694.90|
|<br>Durbin-Watson<br>stat|0.811||||

GDITOY = Gross domestic investment (% to GDP) FDITOYHATP = Foreign direct investment (% to GDP) GOVYCHATP = Government consumption (% to GDP) GOVYKHATP = Public capital expenditures (% to GDP) STATEFAILAVG = Average of "Episodes of State Failure" as defined by the State Polity 98 from the University of Maryland. It includes Ethnic Wars, Revolutionary Wars, Abrupt or Disruptive Regime Changes, and Genocides/Politicides.

VOL 12DLGTRLCRHATP= 12 month moving window of a standard deviation of the first difference of the log of the real global total return series.

GROHATP = GDP growth.

Note: Variables with the suffix HATP are instrumental variables; they are derived by running a panel regression for each (except statefailavg) independent variable (Xi, ) on the other independent variables; the residuals from each panel regression are saved and the fitted values are calculated by subtracting the residuals from the dependent variable. These fitted values are the ones used in the above estimation.
**Figure 2: Cost-effectiveness of improving learning – Results re-ordered by impact values possible within 90 percent confidence interval**

|**Programs ordered by**<br>**decreasing cost-effectiveness**|**Programs re-ordered by**<br>**plausible cost-effectiveness**<br>**(using confidence intervals)**|**Additional**<br>**SD per $100**|
|---|---|---|
|Providing earnings information,|Individually-paced computer|1.55|
|Madagascar|assisted learning, India||
|Streaming by achievement,|Village-based schools,|2.13|
|Kenya|Afghanistan||
|Linking school committee to|Remedial education, India|3.07|
|local government, Indonesia|||
|Electing school committee and|Linking school committee to|34.62|
|linking to local government,<br>Indonesia|local government, Indonesia||
|Teacher incentives (year 2),<br>Kenya|Streaming by achievement,<br>Kenya|34.78|
|Textbooks for top quintile,<br>Kenya|Providing earnings<br>information, Madagascar|118.34|
|Remedial education, India|Camera monitoring, India|2.28|
|Camera monitoring, India|Read-a-Thon, Philippines|1.18|
|Village-based schools,<br>Afghanistan|Teacher incentives (year 2),<br>Kenya|6.29|
|Extra contract teacher +<br>streaming, Kenya|Electing school committee<br>and linking to local<br>government, Indonesia|13.34|
|Individually-paced computer<br>assisted learning, India|Textbooks for top quintile,<br>Kenya|3.56|
|Girls’ merit scholarships, Kenya|Minimum conditional cash<br>transfers, Malawi|0.06|
|Read-a-Thon, Philippines|Extra contract teacher +<br>streaming Kenya|1.97|
|Minimum conditional cash<br>transfers, Malawi|,<br>Girls’ merit scholarships,<br>Kenya|1.38|

Source: Data adapted from J-PAL (2014a). Calculations by authors. Note: This is an example of one of many possible re-orderings, which is consistent with the 90 percent confidence interval costeffectiveness estimates. It is achieved by re-ordering the 7 programs with the highest point estimates in descending order of the lower bound of cost-effectiveness, and the 7 programs with the lowest point estimates in ascending order of the upper bound.

25
9 Table 3: Direction of Trade in a Gravity Model (

||Proje|cted Expo|rts|Actua|l**_1992193_**|Exports|
|---|---|---|---|---|---|---|
||Estonia|Latvia|Lithuania|Estonia|Latvia|Lithuania|
|EU|**_51_**|56|58|**5**|25|**17**|
|EFTA|26|20|16|30|9|**_5_**|
|**-Finland**|**11**|5|4|21|2|1|
|-Sweden|8|8|_5_|8|6|3|
|EEC|2|2|2|4|_5_|6|
|Other West|14|_15_|16|14|13|2|
|Balies|I|I|I|3|6|7|
|Russia|4|4|4|21|30|28|
|Odter FSU|2|2|3|11|12|33|

Source: COMTRADE, own esinmaxes. Estonian Misny of Tradc, Smta Cominiune of S otf Latyi (1994).

capita; (CIL) increases from L to C in figure 1). Both are presented as ratios of world averages in log scales.' Compared to other developing countries, the Baltics would be relatively well endowed with natral resources and labor, but have much less capital than the resource poor, industrializing East-Asian countries (such as Korea, Hong Kong, Singapore, Taiwan, China) for example. Within Europe the Baltics' present factor endowments put them close to other Eastrn Europeans, especially the Visegrad (Czech and Slovak republics, Hungary and Poland) group. Russia and other FSU countries are relatively more abundant in land resources.

The factor-endowment triangle does not differentiate by type of labor, which is likely to be an important determinant of trade flows in the Baltics. Comparisons of relative skill and capital endowments can give a better picture of the Baltics' relative endowments especially in services and manufactures compared to their main competitors. Figure 2 presents relative endowments of skills (measured by years of schooling) and per capita income for the Baltics, a number of developing countries likely to compete with them in world markets, and industrial 'It is difficult to find suitable pmxies for the factor endowments, and therefore the figure should be read as indicative only. The land proxy, arable land, is likely to overestimate agricultural resources at the cost of other resources such as minerals, oil, forests etc. Also the capital proxy (GDP) may overestimate the capital base in the FSU countries as much of this is likely to be worthless at international prices.