rafmacalaba/datause-encoder-data huggingface dataset:
The results from the Klein‐Vella (2009) approach are reported in Table 4 for the different cases. There are four sub‐samples considered: (4.1) sons 1988, (4.2) daughters 1988, (4.3) sons 2002, and (4.4) daughters 2002. As benchmark, we report estimates from OLS and the normalized Inverse probability weighted propensity score estimator (IPW) due to Hirano and Imbens (2001) and Hirnao et al. (2003). The IPW estimator relies on the assumption that there is no significant selection on unobservables (and also ignores measurement error). The Klein and Vella (2009) estimates can be compared to IPW estimates to get a sense of the role played by selection on unobservables and measurement error.

The first row in Panel A of all four cases in Table 4 shows the OLS estimate of the effects of having at least one parent with more than primary schooling on the years of schooling of children. Consistent with the results earlier, the OLS estimates indicate a significant effect of parental education on both daughters and sons, although the numerical magnitudes are somewhat different because of the change in the set of control variables. The estimates from the IPW are positive across the board, although in general smaller in magnitude (except for the daughters in 2002). An important difference with the OLS results is that the parental effect in IPW becomes insignificant at the 10 percent level for sons in 1988 (see row 2).

Rows (3) and (4) in panel A of Table 4 report the estimated causal effect of parental education on children’s years of schooling from two alternative specifications of the heteroskedastic probit model used for generating the instrument.<sup>33</sup> Row (3) corresponds to the case when the variables included in<sup>_X_</sup> _i_<sup>are all the parental characteristics including the county fixed effects. The point</sup> estimate for sons in 1988 is negative, but it is positive for daughters. However, the estimated effects in 1988, for both sons and daughters, have wide confidence intervals, and the null of no causal effect

> 33 The Klein and Vella (2009) estimates reported here are from the STATA program written by Millimet and Tchernis (2010). The confidence intervals are boostrapped using 250 replications.

28
# **Table 4**

# **EXPORT GROWTH DECOMPOSITION: BRAZIL (FIRST QUARTER 2005 TO THIRD QUARTER 2011)**

|<br>|**Export**<br>**growth**<br>**(%)**|**Export**<br>**market**<br>**share**<br>**change**<br>|**Performance**<br>**(export growth**<br>**without**<br>**composition**<br>|**Pull fa**<br>**(speciali**<br>**compositio**<br>**of wh**|**ctors**<br>**zation,**<br>**n effects),**<br>**ich:**|**Push fac**<br>**growth wit**|**tors (export m**<br>**hout compositi**<br>**which:**|**arket share**<br>**on effects), of**|
|---|---|---|---|---|---|---|---|---|
|||**(%)**|**effects) (%)**|**Geo-**<br>**graphical**<br>**(%)**|**Sectoral**<br>**(%)**|**Overall**<br>**(value)**<br>**(%)**|**Price**<br>**component**<br>**(%)**|**Volumes**<br>**component**<br>**(%)**|
|**G-3**|||||||||
|**European Union (EU27)**|6.7|-0.8|7.7|-0.8|-0.2|-2.0|-0.9|-1.1|
|**—of which Euro Area**<br>**(EA17)**|6.4|-3.5|7.5|-0.8|-0.3|-2.2|-1.0|-1.2|
|**Japan**|6.2|-3.8|5.5|1.7|-1.1|-4.2|0.5|-4.6|
|**United States**|7.4|-1.9|5.6|1.6|0.3|-3.6|-0.4|-3.2|
|**BRICS**|||||||||
|**Brazil**|14.9|5.6|11.7|2.1|1.1|1.8|2.6|-0.8|
|**Russian Federation**|12.4|3.0|8.4|0.9|3.1|-1.2|0.6|-1.8|
|**India**|14.9|5.5|14.4|0.4|0.0|4.6|-1.7|6.4|
|**China**|17.3|7.9|20.1|-0.2|-2.6|10.3|1.4|8.8|
|**South Africa**|12.1|2.7|10.6|0.9|0.6|1.0|0.1|0.8|
|**MIST**|||||||||
|**Mexico**|9.9|0.5|13.3|-4.7|1.3|4.1|1.4|2.6|
|**Indonesia**|12.5|3.1|10.8|0.3|1.5|1.1|0.3|0.8|
|**Korea, Rep. of**|11.4|2.1|11.1|2.5|-2.2|1.6|-2.5|4.1|
|**Turkey**|**11.8**|**2.4**|**13.1**|**1.0**|**-2.4**|**3.6**|**0.6**|**3.0**|

_Source:_ Authors’ calculation. _Note_ : Figures are in average annual growth.

18
large degree of variation in external and preferential tariffs in our sample, even when we look at average levels.

# **III.3 – Aggregate trade**

We now look at aggregate trade data for the six original ASEAN members to examine how trade patterns change following the implementation of trade preferences.<sup>9</sup> The purpose is to identify whether there are significant trend changes in the trade growth with members and nonmembers subsequent to the agreement.

Figure 2 shows the share of imports that stems from ASEAN members, collectively and for each of the six individual countries, 13 years before and after the introduction of preferences in 1993. The figures show that, for the bloc as a whole and for most members, the share of imports coming from partner countries has increased steadily since 1993, although no clear trend is visible before that year. This suggests that the preferences were effective in affecting trade patterns. However, it does not hint at whether this reflects the prevalence of trade diversion or trade creation, since both imply an increase in the share of preferential imports.

In figure 3 we then plot, for the same period, the volumes of intra-AFTA imports and imports from outside the bloc. The figures suggest no evidence of important trade diversion, as imports from AFTA nonmembers kept increasing after the formation of the bloc in most years for all countries.10 Imports from within the bloc increased at a higher pace, though, which explains the rise in the share of intraAFTA trade. These trends are consistent with trade creation dominating trade diversion in AFTA, although other explanations are also plausible. The figures also make clear that import growth with members and with nonmembers are highly correlated, implying that other factors besides AFTA are important drivers of aggregate trade growth.

> 9 Trade data for the four late entrants are available only for recent years.

> 10 The important exception is 1997, where trade flows fell significantly. That drop was largely unrelated to AFTA, however, reflecting instead the Asian currency crisis. In fact, in that year imports from members and nonmembers fell alike.

13
**6.** Figure Net Permanent and Long-ierm Migration in New Zealand and Australia **(in** Thousands)

<!-- Start of picture text --> 160 30<br>140- ,'''N/X X _ < _j ,, /\lGls.ew ZeAlan4 20<br>0~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~1<br>o 100- . *<br>z -1~~~~~~~~~~~~~~~~~~~0<br>0 I~ ~ ~ ~~~~~~~~~~~~~~2<br>I~~~~~~~~~~~~~~~~~ '<br>40 ji -- 30<br>20- - -40<br>0 _ I - I - S50 I I --- I I - - 1 L -<br>1945 1950 1955 1960 196N 1970 1975 1980 1985<br>March Year (NZ) June Year (Aus)<br><!-- End of picture text -->

**Source: Poot,** 1986: **_Figure_** _3._
Table 2: Evidence on Gender Effects on Household Expenditures, continued

|Study|Methodology and Data<br>Source|Outcome<br>Variables|Measure<br>of<br>Female<br>Resources|Effect<br>of<br>Increase<br>in<br>Female Resources|
|---|---|---|---|---|
|Kennedy and<br>Peters (1992)|Cross-sectional data,<br>Kenya and Malawi|Expenditure<br>shares|Female versus<br>male headed<br>households|In female-headed<br>households, smaller<br>expenditures on alcohol<br>and a larger expenditure<br>share for food.|
|Khandker<br>(2005)|Quasi-field experiment<br>in Bangladesh|Total<br>expenditures,<br>schooling|Woman's<br>borrowing<br>versus man's<br>borrowing|In households where the<br>woman has borrowed, food<br>as well as non-food<br>expenditures are larger.<br>Also a positive effect on<br>girls' schooling.|
|Lundberg,<br>Pollak, and<br>Wales (1997)|Natural experiment with<br>child allowance, U.K.|Expenditure<br>shares|Woman's<br>income share|Expenditure on children's<br>and women's clothing<br>increases relative to men's<br>clothing.|
|Phipps and<br>Burton (1998)|Cross-sectional data,<br>Canada|Expenditure<br>shares|Woman's<br>income share|Expenditures on child care,<br>children's clothing,<br>women's clothing, and food<br>increase.|
|Rubalcava,<br>Terual, and<br>Thomas (2009)|Field experiment,<br>PROGRESA, Mexico|Expenditure<br>shares|Woman's<br>income share|Share of expenditure on<br>children's clothing<br>increases. Food<br>expenditure share<br>decreases in some<br>specifications.|
|Rubalcava, and<br>Thomas (2000)|PSID and CEX, using<br>cross-state variation in<br>the introduction of<br>AFDC|Expenditure<br>shares|Aid paid to<br>single<br>mothers|More aid paid to single<br>women associated with an<br>increase in spending on<br>toys, baby clothing, baby<br>furniture, and health, and a<br>decrease in spending on<br>alcohol, car maintenance,<br>sports entertainment, and<br>food outside the home.|

# **2.7  Summary**

Several patterns emerge from the empirical literature. First, households do not fully pool income, in the sense that the gender of the income recipient matters. Second, higher female budget shares are associated with higher spending on food, higher spending on children, as well as a better nutritional status of children. Third, higher male budget shares are often associated with higher 11
> FIGURE 3.14

<!-- Start of picture text --> Main sources of emergency funds<br>Adults able to raise emergency funds by main source of funds (as % of all adults), 2014<br>0 20 40 60 80<br>East Asia & Pacific<br>Europe & Central Asia<br>High-income OECD economies<br>Latin America & Caribbean<br>Middle East<br>South Asia<br>Sub-Saharan Africa Other<br>Savings Family or friends<br>Working or employer loan Private<br>Financial informal<br>Note: "Other" includes "other sources" and "don't know" and "refuse" answers. institution lender<br>Source: Global Findex database.<br><!-- End of picture text --> to come up with the funds cited savings as their main source of funding, while in all other developing regions people most frequently cited family and friends as their main source.

Among those who cited savings as their main source, only in high-income OECD economies and East Asia and the Pacific did a slight majority indicate that they save at a financial institution. In all other regions alternative ways of saving dominate in this group. On average in developing economies, 56 percent of those who would rely on savings do not save at a financial institution. This share exceeds 60 percent in all developing regions other than East Asia and the Pacific—and it is as high as 78 percent in the Middle East and South Asia. This suggests a large opportunity to design formal savings products to keep savings safe and accessible in the case of an emergency.

Beyond savings and family and friends, money from working or a loan from an employer is another important source of funds. Globally, 14 percent of respondents who reported that it would be possible to come up with the money cited this as their main source, with little regional variation. Not surprisingly, this source was more likely to be cited as the main one by respondents who also reported having received a wage payment from an employer in the past 12 months than by those without wage employment: among respondents saying that it would be possible to come up with emergency funds, this source was cited by 20 percent of those with wage employment and 7 percent of those without it in developing economies—and by 15 percent of the first group and 5 percent of the second in high-income OECD economies. Moreover, in both groups of economies adults with wage employment were on average about 10 percentage points more likely to report that it would be possible to come up with emergency funds. And they were less likely to cite family and friends as their main source of funds.

Some differences by gender are also apparent. Among respondents saying that it would be possible to come up with emergency funds, men and women were equally likely to cite savings as their main source—and also equally likely to have saved formally in the past year—in both high-income OECD and developing economies. But a larger share of women than men within this group cited family and friends as their main source, while a larger share of men than women cited money from working or a loan from an employer.

> THE GLOBAL FINDEX DATABASE 55
plications for quality, enrollment, and equity. _Economics of Education Review, 71,_ pp. 7-22.

- [51] OECD (2018). Education at a glance 2018: OECD indicators. Organisation for Economic Co-operation and Development.

- [52] Page, L. C., & Scott-Clayton, J. (2016). Improving college access in the United States: Barriers and policy responses. Economics of Education Review, 51, 4-22.

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50
Table 6.3: Employment gain multiplier during the Recovery

|||Chang <br>|e in number<br>|of non-trad<br>|ablejobs<br>||
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
|Bartik|0.681||0.783||1.264***||
||[0.619]||[0.485]||[0.356]||
|△tradable jobs||1.682**||2.833**||3.011***|
|||[0.846]||[1.199]||[0.618]|
|Constant|Y|Y|Y|Y|Y|Y|
|Time fixed effect|Y|Y|Y|Y|Y|Y|
|County fixed effect|Y|Y|Y|Y|Y|Y|
|Observations|9,018|9,018|12,013|12,013|15,029|15,029|
|R-squared|0.008||0.008||0.020||
|Number of county|3,106|3,106|3,111|3,111|3,123|3,123|
|Years|2011|-2013|2011-|2014|2011|-2015|

Robust standard errors in brackets, *** p<0.01, ** p<0.05, *p<0.1 Table 6.3 reveals that during the initial four years of the Recovery, 2011 to 2014, there is mixed evidence on the spillover from tradable job gains to other sectors. The employment multiplier ranges from 0 (column 1) to 1.68 (column 2) in 2011-2013, and from 0 (column 3) to 2.83 (column 4) in 2011-2014. When year 2015 is included, there is stronger evidence on the positive spillover from tradable job gains, with the employment multiplier ranges from 1.3 (column 5) to 3.0 (column 6). This suggests that the non-tradable labor market became tighter in 2015 and non-tradable firms started to make new hires.

25
|Electric Power Sector Loan Project||
|---|---|
|Electric Power Sector Loan Project||
|Trade PolicyLoan Project(02)||
|Trade PolicyLoan Project(02)||
|Trade PolicyLoan Project(02)||
|Capital Market Development Project|Stefan Alber-Glanstaetten|
|Capital Market Development Project|Stefan Alber-Glanstaetten|
|Capital Market Development Project|Stefan Alber-Glanstaetten|
|Capital Market Development Project|Stefan Alber-Glanstaetten|
|Social Sector Management Technical Assistance Loan Project|Evangeline Javier|
|Social Sector Management Technical Assistance Loan Project|Evangeline Javier|
|Social Sector Management Technical Assistance Loan Project|Evangeline Javier|
|Social Sector Management Technical Assistance Loan Project|Evangeline Javier|
|Social Sector Management Technical Assistance Loan Project|Evangeline Javier|
|Social Sector Management Technical Assistance Loan Project|Evangeline Javier|
|Decentralization and Improvement of SecondaryEducation Project|Juan Prawda Witenberg|
|Decentralization and Improvement of SecondaryEducation Project|Juan Prawda Witenberg|
|Decentralization and Improvement of SecondaryEducation Project|Juan Prawda Witenberg|
|Decentralization and Improvement of SecondaryEducation Project|Juan Prawda Witenberg|
|Decentralization and Improvement of SecondaryEducation Project|Juan Prawda Witenberg|
|Decentralization and Improvement of SecondaryEducation Project|Juan Prawda Witenberg|
|Decentralization and Improvement of SecondaryEducation Project|Juan Prawda Witenberg|
|Public Enterprise Reform Adjustment Loan(PERAL)Project||
|Public Enterprise Reform Adjustment Loan(PERAL)Project||
|Public Enterprise Reform Adjustment Loan(PERAL)Project||
|HousingSector Project(01)||
|HousingSector Project(01)||
|HousingSector Project(01)||
|HousingSector Project(01)||
|HousingSector Project(01)||
|Road Maintenance and Rehabilitation Sector Project|Gerard L. Liautaud|
|Road Maintenance and Rehabilitation Sector Project|Gerard L. Liautaud|
|Road Maintenance and Rehabilitation Sector Project|Gerard L. Liautaud|
|Road Maintenance and Rehabilitation Sector Project|Gerard L. Liautaud|
|Road Maintenance and Rehabilitation Sector Project|Gerard L. Liautaud|
|Road Maintenance and Rehabilitation Sector Project|Gerard L. Liautaud|
|Road Maintenance and Rehabilitation Sector Project|Gerard L. Liautaud|
|Road Maintenance and Rehabilitation Sector Project|Gerard L. Liautaud|
|Provincial Development Project|Miguel Mercado-Diaz|
|Provincial Development Project|Miguel Mercado-Diaz|
|Provincial Development Project|Miguel Mercado-Diaz|
|Provincial Development Project|Miguel Mercado-Diaz|
|Provincial Development Project|Miguel Mercado-Diaz|
|Provincial Development Project|Miguel Mercado-Diaz|
|Provincial Development Project|Miguel Mercado-Diaz|
|Provincial Development Project|Miguel Mercado-Diaz|
|Provincial Development Project|Miguel Mercado-Diaz|
**Table** **<u>3: Parameter Estimates and Output Elasticities for Alternative Specifications of the Production Function</u>**

|||**Holding level**||**Plo**|**t level**|
|---|---|---|---|---|---|
||**Translog**|**Cobb-D**|**ouglas**|**Translog**|**Cobb-Douglas**|
|Log plot area in hectares|0.308***|0.308***|0.339***|0.266***|0.245***|
||(14.163)|(18.922)|(21.566)|(13.759)|(17.040)|
|Log labor days|0.406***<br>|0.410***<br>||0.501***<br>|0.481***<br>|
||(15.051)|(19.620)||(19.226)|(22.277)|
|Log male family labor days|||0.107***|||
||||(5.510)|||
|Log female family labor days|||0.170***|||
||||(8.346)|||
|Log hired labor days|||0.123***<br>(6.431)|||
|Log chemical fertilizer use in kg|0.133**|0.094***|<br>0.095***|0.151***|0.134***|
||(2.033)|(3.505)|(3.121)|(3.418)|(3.877)|
|Log pesticide use in US$|0.001|0.101***|0.107***|0.148|0.142***|
||(0.024)|(2.696)|(2.823)|(1.477)|(2.893)|
|Log manure use in kg|0.117***<br>|0.118***<br>|0.113***<br>|0.092***<br>|0.102***<br>|
||(5.986)|(7.943)|(7.581)|(4.105)|(5.140)|
|Dummy male family labor|||0.054<br>|||
||||(0.859)|||
|Dummy female family labor|||0.340***|||
||||(3.644)|||
|Dummy hired labor|||-0.129**|||
||||(2.197)|||
|Dummy chemical fertilizer use|0.263*|0.160|0.085|-0.100|-0.128**|
||(1.829)|(1.609)|(1.016)|(1.580)|(2.130)|
|Dummy pesticide use|-0.020|-0.029|-0.076|0.111|0.107|
||(0.220)|(0.327)|(1.101)|(0.836)|(0.833)|
|Dummy manure use|<br>-0.336***|<br>-0.341***|<br>0.418***|<br>-0.229***|<br>-0.235***|
||(8.012)|(8.394)|(4.726)|(6.108)|(6.595)|
|Log(plot area in hectares)<sup>2</sup>|-0.079***|||-0.013||
|<br>|<br>(3.631)|||<br>(0.886)||
|Log(male labor days)<sup>2</sup>|-0.099***|||-0.057*||
|<sup>2</sup>|(2.894)<br>|||(1.907)<br>||
|Log(chemical fertilizer use in kg)|-0.008|||0.012||
||(0.540)|||(0.554)||
|Log(pesticide use in kg)<sup>2</sup>|0.071**|||-0.006||
|<br>|(2.257)|||(0.118)||
|Lo(manure use in k)<sup>2</sup>|-0013|||0015||
|g g|.<br>(0.972)|||.<br>(0.655)||
|Log land X Log labor|<br>0.079***|||<br>0.055***||
||(3.462)|||(3.384)||
|Log land X Log chemical fertilizer|<br>-0.024*|||<br>0.029||
||(1.650)|||(1.185)||
|Log land X Log pesticide use|-0.016|||-0.018||
||<br>(0.478)|||<br>(0.761)||
|Log land X Log manure use|0.022|||-0.002||
||(1.635)|||(0.118)||
|Log labor X Log chemical fertilizer|<br>0.020|||<br>-0.014||
||(1.160)|||(0.415)||
|Log labor X Log pesticide use|-0.060|||-0.015||
||(1.504)|||(0.474)||
|Log labor X Log manure use|<br>-0017|||<br>-0019||
||.<br>(1.005)|||.<br>(0.940)||
|Log chem. fertilizer X Log pesticide use|<br>0.003|||<br>0.050*||
||(0.196)|||(1.713)||
|Log chem. fertilizer X Log manure use|<br>-0.002|||<br>0.006||
||(0.212)|||(0.200)||
|Log pesticide use X Log manure use|0008|||0031||
||.<br>(0.325)|||.<br>(1.155)||

18
because it would help such a country exit this trap (Castelló-Climent and Mukhopadhyay, 2013). Replicating our study using data for other countries, including those with high levels of poverty, denotes an interesting venue for future empirical research.

Future research will hopefully also shed some light on the channels via which IO affects future income growth. It is conceivable that IO alludes to unequal access to good schooling or discrimination in the labor market, to name two candidate channels, either of which signifying inefficiencies that will disproportionally concern the growth prospects of the disadvantaged. A potential positive relationship between IO and income growth for the rich may be harder to explain. One possibility is that the greater the share of the population who are disadvantaged by IO, the greater presumably the supply of labor at lower levels of skills, and the greater, even in a fully competitive market, the skill premium received by those who benefit from better opportunities. We should stress however that the positive effect of IO on growth for high incomes ceases to be significant when using weak instrument-robust inference.

Another empirical question worth exploring is how economic growth or development more broadly impacts on inequality of opportunity. In a preliminary investigation, Marrero and Rodríguez (2016b) find that raising the level of real GDP is associated with lowering levels of inequality of opportunity. If this is confirmed, then this may elude to the possibility of a positive cycle where economic growth lowers IO, and the reductions in IO further stimulate economic growth. However, more evidence on this will need to be obtained, including a precise analysis of the impact of pro-poor (and prorich) growth on inequality of opportunity. Finally, we can think of at least one extension of our analysis, namely one that tracks income growth by individual characteristics such as race (and other factors that are beyond the individual’s control) as an alternative to tracking growth by income percentile. See for example the recent study by Peragine et al. (2014) who develop the notion of the Opportunity Growth Incidence Curve. It is conceivable that unequal societies are more likely to hold back blacks relative to whites, for example, regardless of where they stand on the socio-economic ladder.

20