rafmacalaba/datause-encoder-data huggingface dataset:
# INTRODUCTION

Households headed by women have become an important phenomenon worldwide in the last half of the twentieth century (ICRW, 1988). Brazil is no exception, having experienced a steady increase in female-headed households as a share of total households over the last three decades, especially in large urban areas. In 1987, one-fifth of all households in metropolitan areas in Brazil were headed by women. As in other countries, female-headed households in Brazil are more likely to be in poverty at any point in timne than are male-headed households.' Little is known about the long term consequences of this trend or the implications for anti-poverty programs in Brazil. If the research of Wood, (1989) is any indicator, the consequences are worrisome. Using data from the 1960 and 19980 censuses, he found that as a result of higher poverty rates and higher levels of other risk factors associated with reduced survival probabilities, children born into female-headed households are more likely to die in childhood. While debate continues over the definition of the female-headed household, the causes of the observed increase worldwide, and the normative imnplications of this increase for women and society, the fact remains that addressing the question of poverty alleviation in this generation and the next requires understanding the economic behavior of female-headed householas.

In both developed and developing countries, studies have shown that female-headed households tend to have different demographic, sociological and microeconomic characteristics from male-headed households, and that these differences have major implications for the design of policy and programmatic interventions (ICRW, 1988; Kossoudji and Mueller, 1983). For example, while children in poverty are always at risk, one of the major findings of studies in the United States is that, even controlling for income, children in female-headed households seem to have a lower rate of socioeconomic attainment than children in two-parent families (Garfinkel and McLanahan, 1986; McLanahan and Bumpass, 1988). This research indicates that the same anti-poverty policy interventions may See Goldberg and Cremen, (1990) for a review of this issue in OFCD and East **Euromean** countries.
Table 4: Estimates of the Investment Equation

|Father’s Years of Education|0.124***|0.125***|0.120***|
|---|---|---|---|
||(0.010)|(0.010)|(0.010)|
|Rural (=1)|-0.195***|-0.196***|-0.179***|
||(0.068)|(0.068)|(0.068)|
|Father’s Years of Education_×_Rural|-0.074***|-0.074***|-0.074***|
||(0.011)|(0.011)|(0.011)|
|Number of School Age Children||-0.051***<br>(0.016)|-0.051***<br>(0.015)|
|Ability Index|||0.015<br>(0.023)|
|Ability Index Sq|||0.003<br>(0.002)|
|Constant|0.480***|0.560***|0.418***|
||(0.079)|(0.081)|(0.085)|
|R2|0.058|0.059|0.061|
|Observations|18327|18327|18327|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). The dependent variable is educational expenditure adjusting for inflation. Inflation rates are calculated using price of rice separately for rural and urban areas. All regressions include wave fixed effects. Rural and urban are defined based on location of household at the time of survey. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square.

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**Table 6. Model in levels (contemporaneous)**

|Dependent: Sector Size|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Effective Tariff|**0.92**<br>_2.98_|||||**-2.82**<br>_2.62_|
|Imports Penetration||**-1.14**<br>_4.49_||||**-4.45**<br>_3.76_|
|Tenure|||**1.66 *****<br>_023_|||**1.06 *****<br>_022_|
|Overtime|||_._|**-11.50 *****<br>_194_||_._<br>**1.09**<br>_156_|
|Union||||_._|**16.56 *****<br>_2.65_|_._<br>**12.57 *****<br>_2.78_|
|C|**80.40 *****<br>|**80.52 *****<br>|**77.31 *****<br>|**84.84 *****<br>|**76.98 *****<br>|**75.80 *****<br>|
||_0.14_|_0.31_|_0.43_|_0.76_|_0.53_|_1.11_|
|R2 (Weighted)<br>|0.995|0.994|0.995|0.994|0.994|0.994|
|R2 (Unweighted)<br>Durbin Watson|0.96<br>0.70|0.96<br>0.69|0.96<br>0.75|0.96<br>0.71|0.97<br>0.75|0.97<br>0.75|
|Dependent: Industry Differential|1|2|3|4|5|6|
|Effective Tariff|**0.06**<br>_0.07_|||||**0.09**<br>_0.09_|
|Imports Penetration||**-0.36 *****<br>_0.14_||||**-0.42 *****<br>_0.10_|
|Tenure|||**-0.02 ***<br>_001_|||**-0.03 ***<br>_002_|
|Overtime|||_._|**0.03**<br>_0.06_||_._<br>**0.09**<br>_0.07_|
|Ui|||||**003**|**021**|
|non|||||**.**<br>_010_|**.**<br>_014_|
|C|**4.12 *****|**4.15 *****|**4.17 *****|**4.11 *****|_._<br>**4.12 *****|_._<br>**4.13 *****|
||_0.00_|_0.01_|_0.03_|_0.02_|_0.02_|_0.05_|
|R2 (Weighted)<br>|0.999<br>|0.998<br>|0.998<br>|0.999<br>|0.999<br>|0.998<br>|
|R2 (Unweighted)<br>Durbin Watson|0.95<br>1.15|0.95<br>1.09|0.95<br>1.12|0.95<br>1.15|0.95<br>1.14|0.95<br>1.08|
|Dependent: Creation|1|2|3|4|5|6|
|Effective Tariff|**0.38 *****<br>_0.13_|||||**0.46 *****<br>_0.11_|
|Imports Penetration||**-0.65 *****<br>_010_||||**-0.51 *****<br>_009_|
|Tenure||_._|**-0.05 ***<br>_0.03_|||_._<br>**-0.06 ****<br>_0.03_|
|Overtime||||**-0.16 ****<br>||**-0.38 *****<br>|
|Union||||_0.07_|**-0.16**|_0.07_<br>**-0.02**|
||||||_012_|_013_|
|C|**0.40 *****|**0.46 *****|**0.51 *****|**0.48 *****|_._<br>**0.45 *****|_._<br>**0.70 *****|
||_0.01_|_0.01_|_0.06_|_0.03_|_0.03_|_0.04_|
|R2 (Weighted)<br>R2 Uihtd|0.781<br>060|0.770<br>059|0.757<br>060|0.768<br>060|0.761<br>060|0.808<br>060|
|(nwege)<br>Durbin Watson|.<br>1.64|.<br>1.69|.<br>1.68|.<br>1.63|.<br>1.65|.<br>1.74|
|Dependent: Destruction (U)|1|2|3|4|5|6|
|Effective Tariff|**-0.001**|||||**-0.001**|
||_0003_|||||_0004_|
|Imports Penetration|_._|**-0.003**<br>_0005_||||_._<br>**-0.006**<br>_0005_|
|Tenure||_._|**0.001**<br>|||_._<br>**0.001**<br>|
|Overtime|||_0.001_|**-0.004**<br>_0003_||_0.001_<br>**-0.003**<br>_0003_|
|Union||||_._|**0.006 ****<br>_0003_|_._<br>**0.003**<br>_0003_|
|C|**0.012 *****|**0.012 *****|**0.010 *****|**0.013 *****|_._<br>**0.011 *****|_._<br>**0.012 *****|
||_0.000_|_0.000_|_0.001_|_0.001_|_0.001_|_0.002_|
|R2 (Weighted)<br>R2 (Unweighted)|0.807<br>0.37|0.797<br>0.37|0.812<br>0.38|0.806<br>0.37|0.812<br>0.38|0.792<br>0.39|
|<br>Durbin Watson|1.86|1.87|1.87|1.86|1.87|1.89|

Note: For all models, the number of included observations is 20, the number of Cross-section included is 18 and the total pool observations are 360.

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- it had a record of bankruptcy or liens files on its property due to payment delinquencies.

As shown in Table 10, by these criteria a substantially higher percentage of lower-income households have bad credit records that would likely exclude them from prime loans than do higher-income households. In addition, substantially higher percentages of African-American and Hispanic respondents had bad credit records than did white respondents. **Table 10 Incidences of Impaired Credit Records**

|**Family characteristic**|**Percentage**<br>**with "bad"**<br>**credit**<br>**Record**|**Percentage**<br>**with "good" credit**<br>**record**|**Percentage**<br>**with**<br>**insufficient informa**<br>**tion**<br>**to classify**|
|---|---|---|---|
|All families|30|57|13|
|_Income (1998 dollars)_||||
|Less than 25,000|36|No data reported|No data reported|
|25,000-49,999|33|No data reported|No data reported|
|50,000-64,999|25|No data reported|No data reported|
|65,000-75,000|22|No data reported|No data reported|
|_Race or ethnicity of respondent_||||
|African-American|48|36|16|
|Hispanic|34|51|15|
|White|27|61|12|

Source: Freddie Mac Corporation (1999) In a national household survey conducted in 2001, the Federal Reserve found a similar pattern linking household incomes to indicators of credit risk. As shown in Table 11, lower-income households were much more likely to have such indicators than were higher income families.

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Several interesting findings emerge. First, higher rice prices are associated with lower participation in wage work for people ages 6-15 and 16-24 (column 1). The magnitude of the effect of rice prices on wage work is especially large for young adults ages 16-24. For example, the 30 percent increase in the price of rice is associated with a 4 percentage point decline in participation, which amounts to a 28 percent decline in the probability a woman ages 16-24 works for a wage from her 1993 base. The 30 percent increase in the price of rice is associated with 6 percentage point decline in the probability that a man ages 16-24 works for a wage. This corresponds to a 30 percent decline in the probability that a man ages 16-24 works for a wage from his 1993 base. Interestingly, this age group experiences the largest increase in school attendance in the VLSS (Nguyen 2002). Moreover, higher rice prices are not associated with statistically significant changes in participation in wage work for older adults.

Second, column 2 of table 3 considers participation in work within the household.

Higher rice prices are not associated with changes in participation in work within the household for girls, but are associated with increases in participation for boys ages 6-15. This increase in work within the household for boys appears to be driven by an increase in participation in household production rather than an increase in market work within the household (columns 3 and 4). Moreover, there is no association between rice prices and participation in work within the household for adults ages 16-24. However, adult women ages 25-64 experience a decline in work within the household (bottom panel, column 2). This decline is mostly driven by the decline in adult female participation in household production associated with rice price increases (column 4).

Overall, column 5 suggests that rice price increases are not associated with changes in market work for children, but are associated with declines in participation in market work for 27
de...ned as wages above what workers would earn elsewhere in the labor market. Because of the nature of their data, Kaplan and Verhoogen are able to decompose plant level wages into a component that re‡ects skill composition and a component that re‡ects wage premia. Their identi...cation strategy is based on the peso devaluation of 1994 that they argue di¤erentially a¤ected incentives to export within industries. Comparing across plants within industries, they ...nd that roughly two thirds of the higher level of wages in larger, more productive plants are explained by wage premia and that nearly the entire di¤erential within industry wage changes induced by the shock to exports is explained by wage premia and not by skills. The authors conclude that sorting on individual ability is not responsible for the well-documented correlation between exporting and wages.

As the ...rst contribution in the literature to account for both ...rm heterogeneity and intermediate trade in their analyses, Amiti and Davis (2008) o¤er a theoretical and empirical examination of the impact of tari¤ cuts on workers’wages that accounts for the extent of a ...rm’s engagement in international trade. Using Indoneisan manufacturing census data for 1991-2000, a period that encompasses Indonesian trade liberalization, the authors develop a general equilibrium model to estimate this relationship. They ...nd that the impact of a given tari¤ change on a ...rm’s workers’wages is dependent upon that ...rm’s role in the global economy. That is, a 10 percentage point decrease in output tari¤s will lower wages of importcompeting ...rms by 3 percent but will raise wages at exporting ...rms by 3 percent. Likewise, a decrease in input tari¤s by 10 percentage points will increase wages by 12 percent at ...rms that rely on imports but will have an insigni...cant impact on the wages of ...rms that rely on a domestic supply.

As Amiti and Davis (2008) summarize, their ...ndings show that trade liberalization raises wages for workers at ...rms that are most globalized and lowers wages at those ...rms that are either marginalized in the global economy and/or oriented toward the domestic market. This provides some con...rmation for the ideas in theoretical work such as Egger and Kreickemeier 32
Consistent with the focus of IKP on the most marginalized, differences were quite large for ST participants; Table 9 replicates the results above for STs only, indicating an additional gain of _187 percent_ over ST non-participants (and the gain is significant despite a small sample size).

**Table 9: Changes in total monthly pc expenditures for poorest early joiners and matched non-** **<u>participants, Scheduled Tribes, 2004 to 2008</u>**

|_Total monthly per capita expenditures,_<br>_2004 Rs._|_Poorest_<br>_early_<br>_program ST_<br>_participants_|_Poorest_<br>_program ST_<br>_non-partic.,_<br>_matched_|_Difference_<br>_ST partic._<br>_vs. non-_<br>_partic._|
|---|---|---|---|
|2004|347|375|28|
|2008|543|443|100|
|_Difference 2004-2008_|196|68|128*|
|Percentage change over non-participants|||187%*|
|Annual rate ofgrowth|10.4%|3.8%||

Table 10 illustrates how we control for any remaining impact of other factors in the regressions, referring to the case in Table 8. Female headship decreases expenditures per capita while higher education levels have a positive impact, but none of the controls that were also included in the matching are significant, as expected, indicating that their effects on expenditures have been captured through the propensity score matching process.

**Table 10: Impact of the program on DID for total monthly per capita expenditures, early versus never joiners,** **<u>poorest households</u>**<sup>**19**</sup>

|`DID`|`total monthly pc`<br>`expenditures`|
|---|---|
|`Early vs. Never`<br>|`71.06*`<br>`(39.4)`|
|`Backward Caste(`_`vs. ST/SC`_`)`|`65.4`<br>`(55.7)`|
|`Other Caste`|`9.3`|
||`(83.0)`|
|`Highest years of schooling in the HH 2004`|`1.9`<br>`(6.6)`|
|`No. members who can write in the HH 2004`|`10.2`<br>`(24.3)`|
|`Female-headed household 2004 (Y/N)`|`-20.6`<br>`(58.4)`|
|`Participation in village meetings 2004`|`-28.4`<br>`(51.6)`|
|`Benefited from NREGS 2008`|`110.0***`<br>`(42.0)`|
|`Benefited from midday meal 2008`|`-85.5***`<br>`(32.0)`|
|`Benefited form housing program 2008`|`2.3`<br>`(54.7)`|
|`Constant`|`160.7***`|
||`(50.4)`|
|`Observations`|`864`|
|`R-squared`|`0.0309`|

Note: Robust standard errors in parentheses. Significance levels: *: 10 percent, **: 5 percent, ***: 1 percent.

> 19 The number of observations in the table is lower than the total sample size, because nearest-neighbor matching excludes control observations that are not "nearest" a treated observation.  While this "loss" of observations may appear undesirable as it may reduce power, in fact this effect is often minimal, as precision is higher when the two groups being compared are more similar (Stuart 2010).

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14 international remittances, unobservable components in the income equation could bias our estimates for households with international remittances. Because of this, in our analysis of marginal expenditure patterns we will show estimates based on both OLS and instrumental variables.

# 4.  Household expenditure data

As shown in Table 6, the 2005/06 Ghana GLSS 5 Survey (sub-sample) collected detailed information on six major categories of expenditure, and on several subdivisions within each category. While the time base over which these expenditures were measured varied (from last visit for most food items, to last 12 months for most durable goods), all expenditures were aggregated to obtain yearly values. For household durables (stove, refrigerator, automobile, etc), annual use values were calculated to obtain an estimate of the cost of one year’s use of that good. Annual use values were also calculated to obtain an estimate of the one year use value of housing (rented or owned).

Table 7 presents average budget shares devoted to the six categories of expenditure for the three groups of households - those receiving no remittances, those receiving internal remittances (from Ghana) and those receiving international remittances (from African or other countries). On average, each of the three groups of households spends over 60 percent of their budget on two categories of goods that are clearly consumption: food and consumer goods/ durables.

Table 7 also reports differences in average budget shares, and conditions these differences for the income and characteristics of the households. The only differences in average budget shares that are significant after conditioning for household income and characteristics are: (1) households receiving internal remittances (from Ghana) spend more on health and less on
OECD (2008): “ _Women in informal employment. What do we know and what can we do?,”_ in “Is Informal Normal? Towards More and Better Jobs in Developing Countries.” OECD Development Centre Publication.

OECD Family Data Base (OECD FDB) (2014).

Reitz J. G., Phan M. B., Banerjee R. (2014): “Gender equity in Canada's newly growing religious minorities.” _Ethnic and Racial Studies_ , 1-18.

Skirbekk V. (2008): “Fertility trends by social status.” _Demographic Research_ , 18(5), 145-180.

Sobotka T., Beaujouan E. (2014): _“ Two is Best? The persistence of a two-child family ideal in Europe.”_ Vienna Institute of Demography working papers 03/2014.

Testa, Maria Rita (2012): “Family sizes in Europe. Evidence firom the 2011 Eurobarometer survey." _European Demographic Research Papers_ 2012, No. 2. Vienna Institute of Demography, Austrian Academy of Sciences.

Tsani S., Paroussos L., Fragiadaki C., Charalambidis I., Capros P. (2013) : ”Female labor force participation and economic growth in the South Mediterranean countries.” _Economics Letters_ , 120(2), 323-328.

Turkstat Labor Force Survey (1992, 1997, 2002, 2007, 2012).

Uraz A., Aran M. A., Hüsamoğlu M., Okkalı Şanalmış D., Capar S. (2010 _): “Recent Trends in female labor force participation in Turkey.”_ State Planning Organization of the Republic of Turkey And World Bank Welfare and Social Policy Analytical Work Program Working Paper, (2).

World Bank (2009 _): “Female Labor Force Participation in Turkey : Trends, Determinants and Policy Framework.”_ Washington.

World Bank (2012): _“Gender Equality and Development”._ World Development Report.

World Bank World Development Indicators (WB WDI) (2014).

Yavuz, S. (2006): “Completing the fertility transition.” _Demographic Research,_ 15(15), 435-460.

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to randomization inference.

We then use our estimated elasticity of fishing with respect to fuel subsidies to simulate how global fish stocks would change if China reduced its distant water fuel subsidies, which totaled $520 million (3.6 billion yuan) in 2018 (Zhang, 2018). We use data on fish stocks from Costello et al. (2016) to predict changes in the extent to which fishing rates in 18 major ocean regions exceed the level that would produce the maximum sustainable yield, a phenomenon known as biological overfishing. Changes in overfishing closely correspond to changes in steady state fish biomass. Our estimates indicate that reforming Chinese distant water fuel subsidies would lead to remarkable reductions in overfishing and gains in biomass across most ocean regions. For instance, a 50% decrease in distant water fuel subsidies would eliminate overfishing in the Southeast Pacific and the Antarctic Atlantic ocean regions, while also leading to an average 17% increase in steady state biomass across all regions. These findings suggest that reforming fuel subsidies could be an effective strategy for promoting sustainable fishing practices and improving fish stocks in many regions of the world.

While there is a long-standing literature on the economic impact of subsidies, there is relatively little research on the causal impact of fuel subsidies on the fishing industry. The two papers most closely related to ours are Wang et al. (2022) and Sakai (2017). Wang et al. (2022) studies the effects of fuel subsidies and vessel buyback programs on the capacity of the trawler fleet of Zhejiang Province, China. Our paper differs in its primary outcome variable— hours of fishing, compared to measures of capacity such as the probability of scrapping a vessel—and in its scope—all distant water vessels, compared to vessels registered in Zhejiang that use the trawling fishing method in Chinese national waters. Sakai (2017) estimates the relationship between subsidies and fish stocks, rather than fishing effort. Their identification strategy compares countries with more and less subsidy to each other over time, whereas our paper implements a regression discontinuity design.

# **2 Institutional context**

Along with 79 other countries, China subsidizes the fuel of its fishing vessels (Schuhbauer et al., 2020). China does so because its fishing industry is economically and strategically 5
lnYi = α + βSi + γ1EXi + γ2EX<sup>2</sup> i + εi, where lnY is the natural logarithm of monthly wage, S is the number of years of schooling of individual i, and EX and EX<sup>2</sup> are the years of experience and its square. We assumed two years of foregone earnings for those with primary schooling.

The corresponding level of education equation is: lnYi = α + β1PRIMi + β2SECi + β3HIGHER + β4UNIVi + γ1EXi + γ2 EX<sup>2</sup> i + εi, where PRIM, SEC, HIGHER and UNIV refer to dummy variables for primary, secondary, higher and university education, from the formulas: where SPRIM, SSEC, SHIGHER and SUNIV are the total number of years of schooling for each successive level of education.

Data used are for East Asian and Latin American countries. Information about data sources and survey year are given in Table 1. Descriptive statistics are included in Annex 1. A consistent sample across countries is used, with the same variables used and few control variables added, to make the work comparable to the work carried out in advanced countries. That is, the working samples for every country consist of males employed for wages in private or public sector (excluded are day laborers, workers in agriculture, self-employed and employers), who are 25 to 65 years of age. The dependent variable is the logarithm of hourly wage (derived from dividing monthly earnings by the monthly hours worked), except for Mongolia and Thailand, for which the dependent variable is the logarithm of monthly earnings.

Quantile regression analysis also allows us to test certain hypotheses. As mentioned earlier, available evidence from a number of high and upper middle-income countries (mainly from Europe and U.S.) shows rather conclusively that returns increase with quantiles; on the other hand, the very limited evidence from low-income countries suggests that the opposite tends to be the case. We believe that additional evidence from other low and middle-income countries 9
3. Review of education sector

- a. Analysis covers recurrent budget?. capital budget? both?_ Only central government spending on education?_ Only Ministry of Education spending?-

Central and local government spending on education? Non-central government spending on education? private schools?_ private contributions? External spending on education?

- b. Reviews all levels of education_ Only covers education generally. Focuses on Primary.

- Secondary-

Higher_ Other_ Is any attention paid to vocational education and training? Yes/No At which levels?

# 4. Data

- a. Are the data government-generated? Yes/No From government planning documents?_ From actual expenditure/investment documents?

- Bank-generated? Yes/No

- b. Are the data accurate and reliable? Yes/No Not clear/not stated_

- c. Does the report state which data are missing? Yes/No Which?

- d. Does the report state which data are presumed inaccurate/unreliable? For example, ghost workers, inconsistencies between ministry statistics. Yes/No Which?

# B. Sector analysis

1. Are comparisons made with other countries? Yes/No How are the comparator countries selected (neighboring, similar per capita GNP, similar economic structure)?

For which data?

2. Financing Model:

- a. Was an education sector financing model usea? Yes/No For which level(s) of education?

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