rafmacalaba/datause-encoder-data huggingface dataset:
||**Log [Se**|**lf-reported**|**Productivity (X**|**OF/ha)]**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||All crops|All crops|Export crops|Food crops|
|Household Female Labor Use (Number)|-0.028***|-0.028***|-0.007|-0.026***|
||(0.010)|(0.010)|(0.016)|(0.010)|
|Household Child Labor Use (Number)|-0.000|-0.000|0.000|0.001|
||(0.000)|(0.000)|(0.001)|(0.001)|
|Hired Labor Use (Number)|0.004|0.004|0.006|0.003|
||(0.003)|(0.003)|(0.004)|(0.003)|
|Number of Fields Managed by the household|0.010|0.010|-0.023|0.033*|
||(0.015)|(0.015)|(0.017)|(0.018)|
|Total number of crops produced|0.006|0.004|-0.038*|-0.006|
||(0.006)|(0.004)|(0.023)|(0.008)|
|Household used any purchased seed|<br>-0.004|<br>-0.003|<br>-0.005|<br>-0.005|
||(0.002)|(0.002)|(0.004)|(0.003)|
|Plots that Use Chemical Fertilizer (% of total)|<br>0.019***|<br>0.018***|<br>0.030***|<br>0.006|
||(0.006)|(0.006)|(0.009)|(0.005)|
|Plots that Use Pesticide (% of total)|<br>0.083***|<br>0.078***|<br>0.082***|<br>0.061***|
||(0.012)|(0.012)|(0.016)|(0.012)|
|Export crops dummy (1 if Household produced any||0104***|||
|<br>export crop)||.<br>(0.019)|||
|**unexplained**|||||
|Log [Total land area cultivated in ha]|-0.050|-0.055|-0.254**|-0.033|
||(0.040)|(0.041)|(0.103)|(0.050)|
|Household head age (years)|-0.057|0.015|-0.548|-0.259|
||(0.188)|(0.188)|(0.337)|(0.209)|
|Married|0.040|0.033|0.025|0.003|
||(0.066)|(0.064)|(0.089)|(0.079)|
|Divorced/Separated|0.015|0.011|0.013|-0.003|
||(0.030)|(0.029)|(0.041)|(0.033)|
|Widowed|-0.007|-0.023|-0.066|0.010|
||(0.073)|(0.071)|(0.143)|(0.076)|
|Household head has Primary education|-0.026|-0.026|-0.050*|-0.017|
||(0.019)|(0.018)|(0.028)|(0.020)|
|Household head has Secondary education|-0.020*|-0.019*|-0.036*|-0.004|
||(0.011)|(0.011)|(0.019)|(0.010)|
|Household head has Tertiary education|-0.000|-0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Household head disability|0.002|0.003|-0.015|-0.001|
||(0.011)|(0.011)|(0.022)|(0.011)|
|Dependency ratio|0.014|0.004|0.002|0.016|
||(0.046)|(0.045)|(0.065)|(0.055)|
|Household Male Labor Use (Number)|<br>-0.024|<br>-0.026|<br>0.003|<br>-0.004|
||(0.025)|(0.025)|(0.036)|(0.027)|
|Household Female Labor Use (Number)|-0.053|-0.065|0.315***|-0.062|
||(0.063)|(0.063)|(0.117)|(0.070)|
|Household Child Labor Use (Number)|0.012|0.013|0.008|0.007|

12
I INTRODUCTION

# Appendix (continued)

|Country,|Last rubtis|hed census|||
|---|---|---|---|---|
|economy, or<br>territory|Date|Populatiolc<br>(1000) *|Total poputation, mid-1985|Total fertility,<br>1985-90|
|Tuvalu<br>|May 79|7 F|Based on UNPVSR<br>7/89 (official<br>est.)||
|Uganda|Aug 69|9549 F|U.N. 1989 (prelim. version)|U.N. 1989 (preLim. version)|
|United Arab<br>Emirates|Dec80|1043 F|UNPVSR<br>4/87 (official<br>est.)|U.N. 1989 ;prelim.<br>version)|
|United Kingdom|Apr81|55678 F|UNPVSR<br>7/86 (official<br>est.)|U.N. 1989 (prelim.<br>version)|
|United States<br>of America|Apr 80|226546 F|UNPVSR<br>7/86 (official<br>est.)|U.N. 1989 (prelim.<br>version)|
|Uruguay|Oct 85|2955F|Official<br>est. from 1985 census|U.N. 1989 (prelim.<br>version)|
|U.S.S.R.|Jan 89|286717 F|U.N. 1989 (prelim.<br>version)|Based on Monnier 1988|
|Vanuatu|Jan79|IIIF|Based on official<br>1987 est.|USBOC<br>1987|
|Venezuela|Oct 81|14517 F|UNPVSR<br>1/87 (official<br>est.)|U.N. 1989 (prelim.<br>version)|
|Viet Nam|Apr 89|64411F|Bank projection<br>from 1977 census|Bank est.|
|Virgin Islands|Apr80|97 J|USBOC 1985|Based on USBOC<br>1987|
|(U.S.)|||||
|Wake Island|Apr 70|2 F|Based on UNPVSR<br>7/89 (official<br>est.)|-|
|Wallis and<br>Futuna|Mar76||Based on UNPVSR<br>7/89(official<br>est.)||
|West Bank||---|Bank est.|Bank est.|
|Western Sahara|Dec 70|76425 F|UNPVSR<br>1/87||
|Western Samoa|Nov 81|156 F|U.N. 1989 (prelim.<br>version)|Bank est.|
|Yemen Arab Rep.|Feb 86|9274 J|Bank assessment|Bank est.|
|Yemen, People's<br>Dem. Rep. of|Mar 88|2345 F|U.N. 1989 (prelim. version)|U.N. 1989 (prelim.<br>version)|
|Yugoslavia|Mar81|22425 J|UNPVSR<br>4/87 (official<br>est.)|UNPVSR<br>10/88 lofficial<br>est.)|
|Zaire|Jul84|29441 F|U.N. 1989 (prelim. version)|U.N. 1989 (prelim. version)|
|Zambia|Aug 80|5662 F|Bank projection<br>from census|Based on Bank sector report|
|Zimibabwe|Aug 82|7546 F|Bank projection<br>from census|DHS 1988|

Notes: When a source for an estimate is directty cited (e.g.. UNPVSR 7/86), the estimate is taken as is from that publication. An estimate "based on" a source is not reported in that pubtication but derived from data contained in it. When a source is given for _ fertility or mortality estimate prior to 1985-90, the corresponding 1985-90 estimate is a projection from the eartier estimate. Citations for the main documents referred to here (usualty by abbreviations) are in the List of references.

- Nature of population estimate: F - de facto, J a de jure. Not appticabLe. No census has been taken, o. no estimates were used because no projections were made for these small countries.

- a Census was for entire peninsula.

- b 55 peopLe.
and corrective actions. The measure ranges from 0 to 14, where larger numbers indicate greater supervisory powers. _Prompt corrective powers_ measures the extent to which the law establishes predetermined levels of bank solvency deterioration that forces automatic enforcement actions such as intervention, and the extent to which supervisors have the requisite suitable powers to do so. The index ranges from 0 to 6 with higher values indicating more promptness in responding to problems. _Reserve Requirements_ is the average level of reserves that banks are required to hold relative to their deposits and other short-term liabilities _. Financial outflow restrictions_ is the average of three binary variables measuring bank restrictions on lending to non-residents, maintaining accounts abroad, and on banks’ investment abroad. This variable comes from the IMF Annual Report on Exchange Arrangements and Restrictions.

We also control for macro factors that may affect the changes in credit risk of the parents and their subsidiaries. ∆ _VIX_ is the change in the Chicago Board Options Exchange Volatility Index, which measures the 30-day expected volatility calculated from implied volatilities from S&P 500 index options. VIX data is obtained from the Chicago Board Options Exchange (CBOE). ∆ _DEF_ is the change in the default spread measured as the difference in Baa-Aaa yields of US firms. Data come from the interest rate data releases from the Federal Reserve Board **.** Table 2 lists all the variables used in our analysis, provides their definition, data sources and descriptive statistics. Table 3 presents correlations across the variables.

# **3. Empirical Methodology**

# _3.1. Computing distance to default measures between parents and subsidiaries_

Our measure of default risk is the distance to default that comes from the structural credit risk model of Merton (1974). Distance to default is computed as the difference between the 8
To examine the effects of FTA use on import prices, we employ transaction-level import data for Thailand during 2007–2011.<sup>4,5</sup> This dataset enables us to identify not only the date of import, firm, branch, exporting country, and commodity (at the 2007 harmonized system [HS] eight-digit level) but also the tariff scheme (e.g., FTA scheme or MFN scheme) used by the importing firm and branch.<sup>6</sup> We focus on the effects of using the FTA by the Republic of Korea (henceforth Korea) in the ASEAN–Korea FTA (AKFTA). During 2007–2011, Thailand had bilateral and/or plurilateral FTAs with 15 countries.<sup>7</sup> One of the main reasons for selecting the AKFTA is that it entered into force in the middle of our sample period, in 2010. Further, the FTA entered into force at a time that was unpredictable for firms in Thailand because of exogenous events such as the political turmoil. These features enhance the validity of our empirical identification of the effects of FTA use. Another reason for selecting the AKFTA is that 4 In general, the use of exporter-side data in the FTA literature has some problems. For example, data on FTA use for exports are difficult to obtain in the case of FTAs adopting the self-certification system. For more details, see Hayakawa et al. (2013a).

5 This period includes the global financial crisis in 2007/2008. In the estimation section, we address this issue to some extent.

> 6 Although several recent empirical studies have used firm-level trade data (e.g., <u>Amiti, Itskhoki, and Konings 2014; Berman, Martin, and Mayer 2012; Eaton, Kortum, and Kramarz 2011), few have used data that</u> enable identification of tariff schemes. One exception is <u>Cherkashin et al. (2015); however, their dataset</u> covered only the apparel industry, whereas our dataset covers all sectors.

7 These countries are Australia, China, Japan, Korea, India, New Zealand, the Philippines, Brunei Darussalam, Cambodia, Lao PDR, Myanmar, Malaysia, Indonesia, Singapore, and Vietnam.

6
price dependent form with **each** demand depending **on the** prices of **the** other two meat types and the price of all other qoods. Consumer income is included<sup>and</sup> homogeneity is imposed by expressing all prices relative to consumer income.

Estimated Equation for RPBEEF RPBEEF - 105.3 - 3.602 Q1 + .5536 Q2 + 4.794 Q3 + 5.680 RELPBR (59.97) (5.686) (5.691) (5.657) (1.015) + .08101 RELPPORK + 765.7 RELOTH - 7.268 PCDBEEF (.5032) (241.9) (.9613) R<sup>2</sup> _ .751, R<sup>2</sup> _ .714, DW - .721, aY - 14.86, Data - 1974:1 - 1987:4 Estimated Equation for RPPORK RPPORK - 257.2 - 11.77 Ql - 16.69 Q2 - 16.80 Q3 + 3.130 RELPBR (35.11) (3.060) (3.185) (3.307) (.3769) + .9091 RELPBEEF - 186.3 RELOTH - 7.589 PCDPORK (.2020) (142.1) (.7508) R' - .923, N2 - .911, DW - .981, cy - 7.730, Data - 1974:1 - 1987:4 Estimated Equation for RPBR RPBR - 344.6 + 2.379 Q1 + 8.387 Q2 + 8.372 Q3 + .3590 RELPBEEF (25.44) (1.112) (1.258) (1.235) (.05903) + .5151 RELPPORK - 785.9 RELOTH - 6.427 PCDBR (.07357) (71.40) (.4993) R<sup>2</sup> _ .958, R<sup>2</sup> = .951, DW - 1.88, _ay_ - 2.927, Data = 1974:1 - 1987:4 The meat trade component of the meat demand block consists of three equations explaining the net imports (exports) of beef, pork, and poultry. Estimated Equation for MBEEF MBEEF - 39.15 + 136.0 Q1 + 99.67 Q2 + 149.0 Q3 + .7265 TPBEEF (73.43) (42.01) (38.54) (38.45) (.3181) + .4309 MBEEF(T-1) (.1370) R<sup>2</sup> _ .517, R<sup>2</sup> - .469, DW - 2.02,. ay -99.82, Data - 1974:1 - 1987:4 23
January of 2009 (Round 1 or "R1"), and study participants were surveyed three additional times at four-month intervals, which we denote as rounds 2, 3, and 4 (R2, R3, R4).

One key aspect of the RESPECT study that we exploit in this paper is the regular testing of curable STIs. In each round, women were tested for chlamydia, gonorrhea, trichomonas, and mycoplasma genitalium.<sup>8,9</sup> Each of these STIs is curable, and individuals who tested positive for any of them were provided vouchers for themselves and up to five partners for free treatment at the local health clinic. A system was put into place to ensure that the first and second line medicines were always available in those clinics.

Our main outcome of interest is an indicator for whether an individual is infected with any one of the four STIs described above. These STIs are primarily transmitted through unprotected sex with an infected partner and act as biological markers of risky unprotected sex. These STIs are also measures of a woman's reproductive health as they can lead to serious health problems such as infertility, complications during pregnancy, and even death. For example, both gonorrhea and chlamydia can lead to pelvic inflammatory disease (PID), which can damage the fallopian tubes leading to chronic pain, infertility, and ectopic pregnancies. These STIs are also associated with pre-term labor and a higher likelihood of a low-birth weight baby. STIs transmitted to children during birth may lead to blindness and other serious health complications. Finally, STIs may lead to increased risk of contracting HIV.

To compliment these biomarkers, we utilize data on self-reported sexual behavior. During each round, women were asked about the number of sexual partners they had over the past four months. For each partner, data were collected on whether a condom was

> 8 The tests required that vaginal swabs be collected from women (performed by a local nurse after careful explanation and consent—acceptability of swabs did not turn out to be problematic), and the samples were tested using nucleic acid amplification tests (NAAT) at the Ifakara Health Institute microbiology lab. The NAAT tests were chosen because of their high sensitivity.

> 9 HIV, syphilis, and HSV-2 were also tested in R1 and R4; both HIV and HSV-2 are not curable, and syphilis had extremely low prevalence rates in this area.  Mycoplasma genitalium was not tested for in R1; since we do not use R1 in our analysis (as discussed later), it does not affect our main results.

6
53 Figure 6 Age and relative poverty rates (country-wide average poverty rate = 1) <!-- Start of picture text --> 2<br>1.5ae<br>}————<br>05 |<br>children adults < 30 middle age retired<br>— Russia — Poland = Hungary<br>=@= Bulgaria coe Latvia —4- Balatus<br><!-- End of picture text -->

Source: Milanovic (1994).

The real key issue is not whether this change is desirable but whether the shift in priorities, from the elderly towards children, is politically feasible. It is a very difficult change because the elders are a very powerful and vocal political group. The same problem is on the agenda in the developed economies as well. Modest success it has met so far does not bode well for the reforms in transition economies. In addition, the transition economies need to effect this major realignment in conditions where some of them are barely starting to emerge from the depression with levels of income 15 to 20 percent below those of 10 years ago, while others are still mired in depression.
sages were sent to households with children in different age groups. Very often, opinion leaders therefore received a different text than other households message living nearby. For instance, on a given day, an opinion leader may have received a text message about nutrition of her 12 month old child, while a household in her proximity received a message on stimulation of her 4 year old child. To the extent that opinion leaders engage with households in their villages based on the messages they receive, this could have caused confusion, possibly especially among the least educated parents.

We investigate this hypothesis by analyzing whether ECD outcomes are better in cases when parents and the closest leader received the same message (compared to those who received a different message). The point estimate of the difference between those two groups is very small, -0.00 (s.e. 0.09), hence providing no evidence in support of the confusion hypothesis. Note, however, that given the large number of potential combinations of text messages between leader and non-leader households, there are relatively few pairs of leader and non-leader households who received the same text message (80 of a total 1,004 pairs), so that the comparison may be underpowered. Even so, the precisely estimated zero suggests that confusion is unlikely to be a major driver of the negative leader results.<sup>25</sup>

# **5.2 Boycott**

Another potential explanation for the negative leader effect could be that opinion leaders have strong prior beliefs about parenting practices, and may tell people not to believe messages if they go against their own beliefs. Qualitative evidence does suggest that some text messages promoted practices diverging from traditional beliefs.<sup>26</sup> The baseline survey includes a set of five questions about caregivers’ beliefs regarding early childhood development, in particular on stimulation and the home environment. The baseline questions about beliefs are similar to those used at follow-up to capture attitudes toward early

> 25Also recall that the negative leader effect is observed for children from households that do not receive text messages, as mentioned above.

> 26For example, several households said they had learned they could give eggs to children, while before they thought this would hurt them. Other examples include parents stopping to give coffee to kids, abandoning harsh disciplining practices, or letting girls plays with toys traditionally considered to be for boys.

21
alimony.<sup>8</sup> Income is made comparable over time by adjusting for inflation (all incomes are expressed in 2010 prices).

Income growth is evaluated for total household income per capita. Our measures of income inequality and inequality of opportunity are computed using individual income for the highest income earner within their household who are between the age of 30 and 50. The age restriction is included in order to mitigate the life-cycle composition effect on an individual’s income (Roemer et al., 2003; Marrero and Rodríguez, 2012). The motivation for using individual income rather than total family income for the measurement of inequality is that this facilitates the decomposition of total inequality into IO and a residual component.<sup>9</sup> Three different measures of inequality of opportunity are being considered: inequality of opportunity in the acquisition of income; inequality of opportunity in acquiring occupational prestige; and social immobility in occupational prestige.<sup>10</sup> The latter is measured as the correlation between the “occupational prestige” for a given individual and the average “occupational prestige” for individuals from an earlier generation belonging to the same race and the same state where the given individual was born. For the first two measures of inequality of opportunity we assume race and gender as circumstances. For race the following four groups are considered: non-Hispanic whites, Hispanic whites, blacks and others. Unfortunately, the IPUMS USA does not include information on parental education or income.

Race has been shown to be an important factor in determining an individual’s success in life as measured by income or occupational prestige. Initial inequality in wealth between individuals from different racial backgrounds combined with barriers to accessing credit can generate significant degrees of inequality of opportunity (Ferreira and Gignoux, 2011; Marrero and Rodríguez, 2011). There is also evidence of continued differential treatment by race in the U.S. labor market, i.e. racial discrimination (see e.g. Bertrand and Mullainathan, 2004; Lang and Manove, 2011). Unequal opportunities between men and women are equally objectionable (Hederos et al., 2014; Calo-Blanco

> 8 Unfortunately, the IPUMS USA database does not provide data on the individual components of income. 9 Note that IO measures the extent to which individual income is driven by individual attributes that are beyond the individual’s control such as race and gender.

> 10 Occupation prestige is derived from the “occscore” variable that is constructed by IPUMS USA which assigns occupational income scores to observed occupations.

7
other European studies such as the ones used by Blanchflower (1996) and Di Tella (2001). What all these studies have in common is the result of lower levels of well-being for the unemployed.

A reverse causality issue can arise if one is limited to cross section data, as it might be easier for happy people to find a job. If inherently happy people are also more productive, better at work or simply more desirable to employers, then it is happiness that positively influences the chances of finding a job, and not the reverse. One way to isolate the causal impact is to use panel data and observe what happens to individuals’ happiness as they change status. This identification strategy is followed in this paper, and the panel data evidence on labor market transitions proves that the causality goes from labor status to happiness rather than the other way around.

# **2.2 The Social Norm Effect of Unemployment**

Individuals are affected by their employment status, but also by others’ employment. Di Tella _et al_ (2001) are among the first to test the impact of aggregate unemployment on individual’s well-being. They find that people have a preference for lower levels of aggregate unemployment. Their results, to be interpreted in a context of a trade-off between inflation and unemployment (a Phillips curve), show that individuals are also hurt by inflation although its effects are much lower. The finding is consistent with the literature on happiness in the sense that it provides evidence of strong comparison effects.

The classical analysis of Akerlof (1980) has been instrumental in the way economists think about social norms, their sustainability and their effect on individual’s behavior. In his model, a social norm precluding transactions at the market-clearing wage can cause unemployment and still be sustainable if deviation from the norm is costlier, in terms of reputation, than the monetary benefit from adhering to it. The higher the proportion of followers of the norm, the more sustainable it is - as it becomes more costly to deviate. Following this theoretical conclusion, the question that arose was whether or not employment can be considered as a social norm. Supposing it can be, then it can be tested whether unemployment hurts more if one’s reference group has little of it - that is if the norm is not followed.

Clark (2003) provides strong evidence supporting this hypothesis. He finds that unemployment at regional, partner and household level positively, strongly affects well-being when the respondent is unemployed, the effect being higher for men. Backing this hypothesis one finds results from Russian data (Eggers _et al_ , 2006) and from South Africa (Powdthavee, 2006).

Stutzer and Lalive (2004) also test Akerlof’s theory. They instrument for the unobserved social norms by using a referendum on unemployment benefits to extract the voting patterns across localities in Switzerland, to proxy for social norms. To correct for the po5
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