rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5: OLS Analysis of Net Transfer Amount**

|||(1)|(2)|(3)|(4)|
|---|---|---|---|---|---|
|**_Parent Charac_**|**_teristics_**|||||
||Age|-4.397|(103.567)|12.873|(107.494)|
||Age square/100|-14.772|(73.821)|-26.042|(76.321)|
||Father|334.428|(206.879)|319.738|(199.056)|
|_Demorahics_|Widowed|-197.960*|(117.107)|-171.012|(115.615)|
|_gp_|Number of children|22.342|(45.193)|16.288|(43.332)|
||Zhejiang|707.374**|(336.641)|557.533*|(323.821)|
||Urban|248.471|(233.937)|245.186|(239.288)|
||Living withotheradult children|37.868|(185.181)|68.194|(176.818)|
||House owner|-563.412|(486.384)|-625.092|(504.109)|
||_Education (Illiterates omitted)_|||||
||Less than primary education|54.473|(188.363)|0.550|(234.450)|
||Primary school|82.972|(281.356)|46.336|(281.129)|
||Middle school|-83.550|(242.299)|-174.869|(295.173)|
|_SES_|High school and above|-173.389|(427.434)|-312.513|(452.015)|
||P-value for education|0.953||0.912||
||Pre-transfer income (000s)|||||
||For the lowest 1/3 income group|24.746*|(14.701)|22.020|(14.157)|
||For the middle 1/3 income group|14.018|(53.407)|9.771|(53.486)|
||For the highest 1/3 income group|-17.654**|(8.209)|-17.544**|(7.973)|
||P-valueforpre-transfer income|0.104||0.098||
||Health Poor|||229.529|(159.597)|
||CES-D|||-3.524|(13.107)|
||ADL or IADL disability|||-232.873|(158.287)|
|_Health_|Cognition score in [8, 11)|||-89.988|(420.400)|
||Cognition score in [0, 8)|||-235.406|(379.792)|
||Major illness|||49.144|(148.420)|
||P-valueforpre-transfer income|||0.551||
|**_Children Char_**|**_acteristics_**|||||
||Age|45.188|(33.500)|44.621|(33.803)|
||Age square/100|-48.652|(34.362)|-47.573|(34.734)|
||Oldest son|269.309|(358.486)|260.596|(351.774)|
|_Demographics_|Youngest son|13.740|(211.908)|20.508|(224.390)|
||Daughter|-207.249|(126.633)|-226.639*|(133.484)|
||Married|-26.839|(339.553)|-32.229|(323.768)|
||#ofchildren(age<16)|98.688|(78.154)|102.906|(78.549)|
||_Education (Illiterates omitted)_|||||
||<br>Primary|-145.349|(170.049)|-154.430|(172.322)|
|_SES_|Middle School|-5.779|(187.153)|-5.304|(186.350)|
||High school and above|859.219***|(264.286)|839.743***|(277.622)|
||P-valueforSES|0.008||0.013||
|CountyDummie|s|Yes||Yes||
|Observations||2,190||2,190||
|R-squared||0.079||0.082||

Note: 1) The sample includes those who are no younger than 25 and with at least one parent no younger than 60.

- 2) Parent characteristics are from main respondents.

- 3) Clustered standard errors at family level are in parentheses.

- 4) *** p<0.01, ** p<0.05, * p<0.1

28
Table S. Estimates of Demand Price Elasticities for Total Fibers and Cotton.

|!|
|---|
|<br>..<br>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~..........|
|Cl<br>&<br> <br>191 <br>24|
|Stotal<br>Rl<br>n otndmn<br>endi o bnelsi<br>wihespenc**t**opic.Terangd<br>ofelasticitie<br>oema<br> <br>u.........<br>..).**.**or.<br>...**.**................<br>.|
|<br>from th estude uvydi<br>from<br>(-0.1)<br>ntoe0.4 witha miern Demand<br>o .1486<br>and<br>14einof-.3<br> <br> <br> <br> <br> <br> <br>.............<br>.<br>Consum pt.ion|
|Posiley (1pana4)n forl<br>Fh<br>ifrnenetmtsibcuer<br>themgogan hca<br>1<br>area coe0de..<br>inutrialrpeveopn<br>(1978)es<br>Induthepria<br>CounrestiCatton, Dbmoth954<br>ofwih020honiTbeS<br>'10.flS.W~......<br>Total fiber<br>ad cotton demad tends.to. b<br>.<br>ne.s.c.ithresec<br>toprc<br>The rang.ofelatictie<br>fro te tuie srvye<br>i fom0<br>0o0 4 it amen latiit<br>o - 2 ad<br>mdin<br>f 02|
|Whie hedemndf**o**<br>c to ieielastin,<br>Cottonl Dewetmand**s**<br>1 95eabs4ue.vluofth<br>Adams &b<br>loebrmtan (1976)sar o wovrldotton export (1%)eTisisevd.ence<br>.<br>agis<br>23eeisec<br> <br> <br> <br>.<br> <br> <br>Possible<br>eplanation for the.. difrnesi.simts.cud.h.eorpia.<br>ae<br>oerd(..<br>industrial<br>or developing<br>countries) and the period of estimation, both of which are shown in Table5.~~~~~~|

ofanueiguppobe (1f8 fro expamoio cottonpe outo from Hoee,26ioaviec1SSA.

canbe eneate trouh smu.ti nite wrd cotonmtesad Mi h slts 194f thes ar3epre4ih following sections.~Cnsumtio Whiale thber deandf cotton ieadtnstob inelasti,oronyc fwit restimates pise The absolutefvaluetofithes elasticity lower than the SSA share of world cotton exports (14%). This is evidence agains **t** he existence. of an adding-up problem from expansion of cotton production from SSA. However, additional evidence can be generated through simul ations of a world cotton model and the results of these are reported in the following sections.

10
underreported statistics (MacFeely, 2018). Certainly there may also be cases where they do not collect certain statistics because of the lack of relevance to their country contexts, as noted in the case of child marriage and female genital mutilation, but we do not find evidence to support this as a driving factor of the result that poorer countries do as well as high-income countries in reporting SDG gender-related indicators.

Beyond these factors, we are left with unexplained variation in gender data availability across countries. This is partly captured in the notable over (and under) performance in reporting gender-related SDGs relative to statistical system strength. One can take a somewhat optimistic perspective in combining this with the two previous findings – that some portion of underreporting is not driven by lack of data but by under-reporting, and that country income is not driving higher rates of reporting. Even without major investments in statistical systems or the years it may take for such investments to yield results, with some concerted effort, it is possible to achieve big wins in SDG gender indicator coverage.

Meanwhile, it is important to note that while the SDG framework offers the world a consensus set of indicators selected as part of a global consultative process, there are other important country-level gender-related indicators available outside the SDG system. Sources such as the UN Women Data Hub and the World Bank’s Gender Data Portal offer compilations of national statistics produced by countries and curated by international agencies.

28
rates of some 5 percent thereafter. Typical was this quote from Economist Intelligence Unit (1<sup>st</sup> quarter 1996): “As the EIU has long been predicting, 1996 is going to be the year in which Russia finally achieves real GDP growth....This opinion is shared by most other forecasters...”<sup>15</sup> Even though real GDP ended up falling by 3.4 percent that year, the fact that inflation was being licked brought in a new element of hope: results reported in Fischer, Sahay and Vegh (1996 p. 59) based on the experience of the transition countries of central Europe indicated that “... stabilization appears close to being both a necessary and sufficient condition for growth”. In January 1997, then first deputy managing director of the IMF Stanley Fischer a meeting at Harvard University and was cited as follows:<sup>16</sup> “Russia, he said, has achieved macroeconomic stabilization....The IMF is virtually certain, he declared, that real growth is underestimated and will soon show up in official figures...” Not long after this endorsement, President Yeltsin, whose re-election in July 1996 had led to firming political certainty, appointed an economic “dream team” spearheaded by two reformers.<sup>17</sup> One could therefore argue that market sentiment had a solid basis for turning positive. This coincided with a progressive relaxation in restrictions on foreign portfolio investment starting in 1997. Thus, perceptions of improving economic prospects and external financial liberalization might explain the big boom in foreign financial participation in 1997.

Another reason might simply be that Wall Street and other financial investors were lulled into complacency by rules-of-thumb, which suggested all was well with Russia’s fundamentals. The government’s debt was steady around 50 percent of GDP, much less than the Maastricht rule of 60 percent, as shown in Table 2; the current account balance hovered around zero and did not

> debt dynamics.

> 15 _EIU Country Report: Russia_ , 1st quarter 1996.  Page 8 forecast real GDP growth of 3 percent for 1996 and 4 percent for 1997.  Actual outcomes were -3.4 percent for 1996 and +0.9 percent for 1997.

> 16 _U.S.-Russian Investment Symposium_ , January 9-12, 1997.  Final Report, p. 3.  Harvard University, Cambridge, Mass.  USA.

> 17 Privatization czar Anatoly Chubais was appointed First Deputy Prime Minister and Minister of Finance, while the reformist governor of Nizhny Novgorod, Boris Nemtsov, was appointed First Deputy Prime Minister and Minister for Fuel and Energy.

15
# **3.1 Equality of Participation**

First, good deliberation must give participants equal opportunity to influence the outcome — at its most basic, this can be captured with a measure of floor time. While the frequency or volume of speech alone may not be a measure of equality, the ability or willingness to speak does reflect one’s authority or standing in the community. By viewing speech as a _social act_ , we follow Karpowitz and Mendelberg (2014), who define speech is “a form of symbolic political or civic participation that may reflect and contribute to the sense of political efficacy and authority — in short, as a political act that creates civic standing” (Karpowitz and Mendelberg, 2014, 5- 6). Understood as a political act, then, speakers’ relative amount of floor time can be a useful indicator of social equality.

Equality, of course, may be defined across multiple axes of difference, including class, race, caste, and gender. While each of these merits consideration, in this study, we focus on gender for three reasons: first, there is a significant body of scholarship that suggests that differences in communication styles may limit women’s ability to be heard, to exercise authority, and to shape outcomes in deliberative setings (Karpowitz and Mendelberg, 2014). In other words, deliberation as a method of collective decision-making may have a gendered component — and it is of normative importance to understand the extent of such differences, and how they can be overcome. Second, concerns of gender equality are perhaps more acute in contexts like rural Tamil Nadu, where this study is located. In such setings, women are ofen deeply disadvantaged across key welfare metrics — from health outcomes to education and labor force participation. For example, female signature literacy in Tamil Nadu is at a mere 64.5 percent in rural areas according to the 2011 census, with male literacy at 82.4 percent.<sup>7</sup> Gaps in labor force participation are even more acute, with rural women employed half as ofen as rural men (31.8 percent versus 59.3 percent).<sup>8</sup> Given that women enter deliberative fora at a disadvantage, then, it is important to understand the ways in which gender — as a description of a person’s social identity, as a dimension of style

> 7“Signature” literacy is defined as the ability to sign one’s own name — another minimalist standard.

> 8Directorate of Census Operations, Government of Tamil Nadu, http://www.tn.gov.in/dear/ Employment.pdf

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# Rural Infrastructure

Investment in rural areas has been a hallmark of the East Asian success story. There has been a more even balance between rural and urban public investment in roads, water, and sanitation facilities in Indonesia, Korea, and Thailand than in Pakistan. The road volume in Pakistan is one of the lowest in the world. Fewer than a third of Pakistan's 45,000 villages have access to wholesale trading centers through the network of all-weather roads. Certain areas remain cut off from the rest of the country, not only during the rainy season and in winter snows, but throughout the year.

Pakistan is poorly supplied with road length relative to both area and population. For instance, the Indian Punjab has a road density of nearly 0.5 km per square km of area, whereas for the Pakistani Punjab, the figure is just 0.3 km.

It is estimated (Qureshi 1993 b) that poorly maintained roads are raising transport costs by 30-40 percent. The distances between villages and marketing and transportation points would not pose a problem if roads were adequate, but the poor state of the rural road network severely inhibits the timely transport of inputs and outputs. Such constraints are likely to be particular important as Pakistan seeks to improve its position in the high value foods sector - such foods often being perishable fruits and vegetables. A recent study (Besque, 1994) using a multiple regression model on Pakistani household data show that higher road status (metal paved or brick paved versus unpaved road) has a positive influence on the production of two key staples -- wheat and rice.

Pakistan faces significant energy supply constraints by comparison with other developing countries. Converting all forms of energy consumption to kilograms of oil equivalent, Pakistan's energy consumption per capita was 243 kg in 1991, below the South Asian average of 289 kg. Electricity power cuts affect the rural sector particularly badly, for up to 10 hours per day. Generators are in widespread use, but studies elsewhere have shown that the cost of power from generators exceeds that of the electricity network by many times. Many pmblems can be traced to Pakistan's energy sector policy, notably underpricing of energy and state control, which has led to crowding- out of public and private capital formation in the sector.

# III. The **Impact of** Past **Policy Distortions**

A comprehensive review of the government's role in agriculture is available in the Pakistan agriculture sector strategy report (World Bank 1994). We concentrate here on issues that have a direct impact on the sustainability of past growth performance.

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required cross-institution clearing could be conducted. Electronic instruments offer a superior service, but in order to provide the best value to the user, such remittance instruments require that payments be processed electronically to the disbursing branches and that interbranch and interbank networks be connected electronically. In Sri Lanka, the lack of electronic interconnectivity between banks and many bank branches means that the recipient may not be able to receive the remittance at a bank branch. Indeed, Sri Lanka’s branch network is not fully maximized (Table 6).

A notable example of the efficient sharing of an electronics platform (not only within one country but across an international border) is the Federal Reserve Automated Clearing House (FedACH) International (Box 2). The service is economical because it connects the existing payments infrastructure in both countries.

Hernandez-Coss (2005) rightly comments that it is too soon to know the impact that FedACH will have on the U.S.-Mexico remittances market. However, for smaller banks that do not have the capital to invest in their own systems networks, it presents an opportunity to become full participants in the market. In the case of the largest banks that have developed their own systems, the time required for a transaction through FedACH may be too slow compared to that required to complete an operation through their own system. One notable aspect of the Mexican Service is that it publishes the foreign exchange rate and spread on website, including an archive of previous rates, thereby adding an important element of market transparency not present in most other services.

15
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well as the benefits received by employees under these schemes, and severance and termination pay. Our proxy is the average of industry wages over a fiveyear period.

## **Lending rates**

Data on lending interest rates, a proxy for the cost of capital, come from the International Financial Statistics data-set of the IMF. The measure is defined as the annual average of the national lending rates.

## **TFP**

Data on total factor productivity (TFP) has been obtained from Klenow and Rodriguez-Clare[21], who estimate TFP by substracting estimates of human and physical capital per worker from GDP per worker.

The resulting sample covers 73 developing and developed countries over the period 1980-2000. Table 1 presents the summary statistics.

[Insert Table 1 about here]

# **5.3 Descriptive analysis**

Table 2 presents the list of countries that are located at the top and the bottom of the distribution of countries ranked according to their net exports of computers in 2000. For these countries the table reports their net exports of computers, their capital/labor ratios, their skilled-labor/labor ratios, and the positions the countries occupy in the rankings for each of these variables. Each ranking ranges from 1-73.

[Insert Table 2 about here] To assess if the data supports our theory, we compare the positions countries occupy in the net exports and relative factor endowments distributions. According to the standard theory, if the production of computers is capital (skilled labor) intensive, we should expect to observe countries that are relatively more abundant in this factor to be located at the top of the net exports of computers distribution.

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The presence of JA trade costs (in the case of the U.S. mainland), and tariffs (in the case of ROW) means that the data shares of purchases from those sources are larger than the distribution weights. The distribution weights can be uncovered by dividing the trade shares by the trade costs associated with an origin and product _θj,t_<sup>_l_=</sup> (1+ ̃ _τSj,t_<sup>_l_</sup> _<u>j,</u>_<sup>_l_)</sup> _t_<sup>1</sup><sup>_−σl_.The data required for this transformation - the values of</sup><sup>_τ_ ̃</sup><sup>_l_</sup> _j,t_<sup>and</sup> _σ_<sup>_l_</sup> - are also applied where necessary in _P_<sup>�</sup> _t_<sup>_l_,andthus</sup><sup>_Pt_.SincepurchasesofPR</sup> output face no trade costs, _θPR_<sup>_l_=</sup><sup>_S_</sup> _PR_<sup>_l_.The</sup><sup>_αl_parameters are the observed share of</sup> sectors _l_ in expenditure in the IO table, whereas the initial values of _E_ ( _P, U_ )<sup>0</sup> are the total expenditures observed in the table, and inflated by GDP growth to 2016.<sup>43</sup> Our calculation of CV is accomplished as follows. Let _U t_ be the numerical value of utility associated with the initial price index _Pt_<sup>0and observed expenditures</sup> _E_ ( _Pt_<sup>0</sup><sup>_,_</sup> _U t_ )<sup>0</sup> . In counterfactual analysis we remove _τ_ ̃ _j,t_<sup>_l_in (16) on imports from the</sup> U.S. mainland, assuming no change in ROW or Puerto Rican domestic prices.<sup>44</sup> Given new values of the price index _Pt_<sup>1,wecalculateanupdatedvalueoftheex-</sup> penditure function _E_ ( _Pt_<sup>1</sup><sup>_,_</sup> _U t_ )<sup>1</sup> . The CV of the price change is calculated as We do this calculation for JA tariff-equivalent trade costs on U.S. imports, and U.S. tariffs on ROW imports. We also conduct the exercise for sub-components of final demand (Consumption, Investment, Government spending, etc.).

> 43We use trade shares and inferred trade costs from 2016. We inflate the older input-output data to produce estimates in 2016 dollars. Puerto Rican nominal GDP grew by a factor of 1.195 between 2006 and 2016.

> 44The assumption of perfectly elastic supply to the Puerto Rican market is reasonable for U.S. and ROW imports. Our calculations implicitly assume no change in the prices of Puerto Rican goods. Puerto Rican domestic prices might also be expected to fall with JA removal, since domestic suppliers would face greater competition from imports (and have access to cheaper imported inputs). Falling domestic prices would raise our estimate of CV. But these additional welfare gains would be partially offset by reduced incomes from reduced domestic sale revenue (absent any additional income gains arising through comparative advantage). We take the assumption of no net change in Puerto Rican prices as a reasonable approximation that facilitates transparent calculations.

38
relatively young. The randomness of the sex of the first-born is in fact widely supported. First, as previously noted, several studies document that sex-selective abortion is much more common among higher-order births. Second, the vast majority of Indian parents want at least one daughter. Third, data from the NFHS indicate that the sex ratio for first births during ten years before the time of each survey is in the normal range. The sex ratio for _first_ births after 1990 (timing of the diffusion of ultrasound testing) is 0.514, which is in the biological range.<sup>34</sup> It is in the normal range even for educated women (known to practice more sex-selection): among educated (uneducated) women the sex ratio for first births is 0.515 (0.513). Section 6.3 provides additional evidence that sex-selection is not practiced at first-birth.

Given possible selective mortality (most visible in the subsample of older women in panel (c) of table 2), the empirical strategy relies on the assumption of _conditional_ exogeneity of the sex of the first-born child. All the regressions for women’s fertility behavior control for a vast set of observable characteristics. The analysis of women’s health status additionally focuses on women who recently had their first birth and are therefore not yet selected.

It is important to note that selective mortality would lead to underestimate the effect of a first-born girl on fertility. This is because women who did not survive (and therefore do not appear in the data) are probably the ones who suffered the most the consequences of an ‘extreme’ fertility behavior due to son preference (both because of poorer health and access to maternal care).

# **5.2 Mechanism: Fertility behavior and sex of the first-born child**

## **5.2.1 Fertility regressions**

Although most Indian women want more than two children, the sex of the first-born still affects the fraction of male children they can have through their lifetime. In fact, as previously noted, more than a third of women want more sons than daughters and more than 40 percent want at least two sons. To examine desired and realized fertility as well as other outcomes that are likely to be affected in contexts with son preference, I estimate the following regression under the assumption of conditional exogeneity of the sex of the first-born.

with woman _i_ , resident in state _s_ , born in year _b_ , surveyed in round _r_ . _yi,s,b,r_ is the dependent variable, alternatively a dummy for whether the woman currently reports the desire to have more children, a dummy for whether she has been sterilized, whether she has undergone ultrasound 34The sex ratio for _all_ births occurred after 1990, including those of higher-order is 0.519.

15